- Tilataso
- MCA
- MCA
- Medication and diseases
- Yhteenveto joku hylkays mukaan
- Biosecurity
- Yhteenveto joku hylkays mukaan
- Breeding management
- Yhteenveto joku hylkays mukaan
- Gestation management
- Yhteenveto joku hylkays mukaan
- Farrowing management
- Yhteenveto joku hylkays mukaan
- Management
- Yhteenveto joku hylkays mukaan
- Housing; breeding unit
- Yhteenveto joku hylkays mukaan
- Housing; gestation unit
- Yhteenveto joku hylkays mukaan
- Housing; farrowing unit
- Yhteenveto joku hylkays mukaan
- Housing
- Yhteenveto joku hylkays mukaan
Tilataso
Data-kansiossa on aineisto.
# load data
setwd("~/GitHub/tilataso")
library(readr)
tilat<-read.csv(file="kategoriset.csv", header=TRUE)Kuvailevat
Valitsen muutaman jatkuvan muuttujan ja muutoin valitsen ne, joissa on alle 6 kategoriaa. Yhteenveto muuttujista:
colnames(tilat)[ apply(tilat, 2, anyNA) ]## [1] "VAR00003" "TII_alusta_5_laatu" "TII_lelukomm"
## [4] "POR_pr_viemar" "VAR00001"tilat<-tilat[ , apply(tilat, 2, function(x) !any(is.na(x)))]
summaryKable(tilat[,1:218]) %>%
kable("html", align = "rrr", caption = "Data variable summary") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px")| Min | 1st Q | Median | Mean | 3rd Q | Max | |
|---|---|---|---|---|---|---|
| Haastrooli_1OmEiosall_2OmOsall_3Esimies | 1.000 | 2.000 | 2.000 | 1.884 | 2.000 | 3.000 |
| Tuotsuunta | 1.000 | 1.000 | 1.000 | 1.488 | 2.000 | 2.000 |
| Karjut_astsiem | 0.000 | 0.000 | 0.000 | 0.419 | 0.000 | 6.000 |
| Tautsu | 0.000 | 0.000 | 1.000 | 0.698 | 1.000 | 1.000 |
| Tautsuok | 0.000 | 0.000 | 0.000 | 0.488 | 1.000 | 1.000 |
| Tautsu_012 | 0.000 | 0.000 | 1.000 | 1.093 | 2.000 | 2.000 |
| Siilotkat | 0.000 | 1.000 | 1.000 | 0.907 | 1.000 | 1.000 |
| Tuhoei | 0.000 | 1.000 | 1.000 | 0.814 | 1.000 | 1.000 |
| Eikulkuih | 0.000 | 0.000 | 1.000 | 0.628 | 1.000 | 1.000 |
| Eikulkuel | 0.000 | 0.000 | 1.000 | 0.698 | 1.000 | 1.000 |
| Suojvar | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
| Suojvarpuh | 0.000 | 1.000 | 1.000 | 0.977 | 1.000 | 1.000 |
| Kadetpesu | 0.000 | 0.000 | 1.000 | 0.698 | 1.000 | 1.000 |
| Toimsiis | 0.000 | 1.000 | 1.000 | 0.953 | 1.000 | 1.000 |
| Saappesu | 0.000 | 0.000 | 1.000 | 0.721 | 1.000 | 1.000 |
| Lasthu | 0.000 | 1.000 | 1.000 | 0.837 | 1.000 | 1.000 |
| Teurkuski_0paaseesikalaan_1eipaase | 0.000 | 0.000 | 1.000 | 0.698 | 1.000 | 1.000 |
| JOU_kertayt_0ei | 0.000 | 0.000 | 0.000 | 0.163 | 0.000 | 1.000 |
| JOU_tuotvaiherill_0ei | 0.000 | 0.000 | 1.000 | 0.721 | 1.000 | 1.000 |
| JOU_pesu_0ei | 0.000 | 0.000 | 0.000 | 0.140 | 0.000 | 1.000 |
| JOU_pesuaine_0ei | 0.000 | 0.000 | 0.000 | 0.093 | 0.000 | 1.000 |
| JOU_desinf_liu_0ei_1liuos_2kuiva | 0.000 | 0.000 | 0.000 | 0.674 | 0.000 | 12.000 |
| JOU_tyhjana_mi1vrk_0ei | 0.000 | 0.000 | 0.000 | 0.302 | 1.000 | 1.000 |
| PORSOSASTO_kertayt_0ei | 0.000 | 0.000 | 0.000 | 0.419 | 1.000 | 1.000 |
| PORS_tuotvaiherill_0ei | 0.000 | 1.000 | 1.000 | 0.767 | 1.000 | 1.000 |
| PORS_pesu_0ei | 0.000 | 1.000 | 1.000 | 0.767 | 1.000 | 1.000 |
| PORS_pesuaine_0ei | 0.000 | 0.000 | 0.000 | 0.233 | 0.000 | 1.000 |
| PORS_desinf_0ei_1LIU_2KUIVA | 0.000 | 1.000 | 1.000 | 2.302 | 2.000 | 12.000 |
| PORS_tyhjana_mi1vr_0ei | 0.000 | 0.000 | 1.000 | 0.605 | 1.000 | 1.000 |
| Raa_0ei_1kontti_2huone | 0.000 | 1.000 | 1.000 | 2.000 | 1.000 | 12.000 |
| Raa_auto_hakee_0ei | 0.000 | 0.000 | 1.000 | 0.628 | 1.000 | 1.000 |
| Raa_viilea_0ei | 0.000 | 1.000 | 1.000 | 0.884 | 1.000 | 1.000 |
| Raa_tuhoelain_1eipaase_0paaseesic | 0.000 | 0.000 | 1.000 | 0.605 | 1.000 | 1.000 |
| Tuhoelmerkkeja_0kylla_1ei | 0.000 | 0.000 | 0.000 | 0.233 | 0.000 | 1.000 |
| Lintuja_0kylla_1ei | 0.000 | 1.000 | 1.000 | 0.767 | 1.000 | 1.000 |
| Tuho_ohjelma | 0.000 | 0.000 | 0.000 | 0.116 | 0.000 | 1.000 |
| kissoja0on1ei | 0.000 | 0.000 | 0.000 | 0.372 | 1.000 | 1.000 |
| Kotielain_sikalaan_0kylla_1ei | 0.000 | 1.000 | 1.000 | 0.791 | 1.000 | 1.000 |
| Vesi_1kunn_0oma | 0.000 | 0.000 | 1.000 | 0.628 | 1.000 | 1.000 |
| Ery | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
| Parvo | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
| Koli | 0.000 | 1.000 | 1.000 | 0.953 | 1.000 | 1.000 |
| Sirko | 0.000 | 0.000 | 0.000 | 0.302 | 1.000 | 1.000 |
| ClC | 0.000 | 0.000 | 0.000 | 0.070 | 0.000 | 1.000 |
| ClA | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| SI | 0.000 | 0.000 | 0.000 | 0.093 | 0.000 | 1.000 |
| APP | 0.000 | 0.000 | 0.000 | 0.116 | 0.000 | 1.000 |
| Loisaika_1ennenpors_2_porskars | 1.000 | 1.000 | 1.000 | 1.372 | 2.000 | 2.000 |
| Uusiryh | 1.000 | 2.000 | 2.000 | 2.000 | 2.000 | 4.000 |
| Ton_tiheys_1aina_2jaetaan | 1.000 | 1.000 | 1.000 | 1.093 | 1.000 | 2.000 |
| Yhdistaggrtmp_1eiongelma_2tmp_3eitmp | 1.000 | 2.000 | 2.000 | 4.349 | 3.000 | 12.000 |
| Muutelkaynn_0ei_1_satunn_2kaynnmuusaann | 0.000 | 0.000 | 1.000 | 0.767 | 1.000 | 2.000 |
| maitokuume | 0.000 | 0.000 | 1.000 | 0.512 | 1.000 | 1.000 |
| metriitti | 0.000 | 0.000 | 0.000 | 0.442 | 1.000 | 1.000 |
| valuttelu | 0.000 | 0.000 | 0.000 | 0.116 | 0.000 | 1.000 |
| mastiitti | 0.000 | 0.000 | 0.000 | 0.233 | 0.000 | 1.000 |
| ontuma | 0.000 | 0.000 | 1.000 | 0.721 | 1.000 | 1.000 |
| syomattomyys | 0.000 | 0.000 | 1.000 | 0.512 | 1.000 | 1.000 |
| kuume | 0.000 | 0.000 | 0.000 | 0.140 | 0.000 | 1.000 |
| loukkaantuminen | 0.000 | 0.000 | 0.000 | 0.372 | 1.000 | 1.000 |
| AB_rutiinilaak | 0.000 | 0.000 | 0.000 | 0.140 | 0.000 | 1.000 |
| Oksitosiini_rutiinisti | 0.000 | 0.000 | 0.000 | 0.395 | 1.000 | 1.000 |
| Kaynnistys_rutiinisti | 0.000 | 0.000 | 0.000 | 0.093 | 0.000 | 1.000 |
| NSAID_porsituksessa_rutiini | 0.000 | 0.000 | 0.000 | 0.233 | 0.000 | 1.000 |
| OMATENSIKOT_0EI_1KYLLa | 0.000 | 0.000 | 1.000 | 0.651 | 1.000 | 1.000 |
| Ensikk_valisiirtkars_ennensiem | 0.000 | 0.000 | 0.000 | 0.395 | 1.000 | 1.000 |
| Ensikk_kiihruok | 0.000 | 0.000 | 0.000 | 0.372 | 1.000 | 1.000 |
| Ensikk_karjukontaktiensi_0hajutainako_1aidanlapi_2kars | 0.000 | 1.000 | 1.000 | 0.953 | 1.000 | 2.000 |
| siemika | 7.000 | 8.000 | 8.000 | 8.070 | 8.000 | 9.500 |
| Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk | 1.000 | 3.000 | 3.000 | 2.884 | 3.000 | 4.000 |
| Kiimantark_ryhmakaytt | 0.000 | 1.000 | 1.000 | 0.884 | 1.000 | 1.000 |
| Kiimantarkalkaa_vrkvier | 0.000 | 0.000 | 1.000 | 1.302 | 1.000 | 5.000 |
| Kiimamerk_emakonselka | 0.000 | 1.000 | 1.000 | 0.860 | 1.000 | 1.000 |
| Kiimantark_postsiem | 0.000 | 1.000 | 1.000 | 0.953 | 1.000 | 1.000 |
| Postsiem_ryhmakaytt_havainnointi | 0.000 | 1.000 | 1.000 | 0.884 | 1.000 | 1.000 |
| Tiin_ultra2 | 6.000 | 6.000 | 6.000 | 6.140 | 6.000 | 10.000 |
| Tiin_ultra_1yhdesti_2kahdesti | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 2.000 |
| Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen | 0.000 | 0.000 | 2.000 | 1.953 | 4.000 | 4.000 |
| Pesantekomatmaara_1runsas_2jnkv_3niukka | 1.000 | 2.000 | 2.000 | 2.093 | 2.000 | 3.000 |
| Sisatutk_ennenoksitos | 0.000 | 0.000 | 0.000 | 0.349 | 1.000 | 1.000 |
| Porsitusaputekn_1empesu_2kaspesu_3kasine_4liukaste | 34.000 | 34.000 | 134.000 | 305.628 | 134.000 | 1234.000 |
| PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa | 1.000 | 2.000 | 2.000 | 1.814 | 2.000 | 2.000 |
| Ruoksu_0ei_1itse_2neuvoja_3kyllaeitietoa | 1.000 | 2.000 | 2.000 | 2.953 | 2.000 | 12.000 |
| Yksilöll_ruokinta | 0.000 | 0.000 | 1.000 | 0.721 | 1.000 | 1.000 |
| AS_1ast_jout_samassa_2asteiole | 1.000 | 2.000 | 2.000 | 1.837 | 2.000 | 2.000 |
| AS_er_os_lkm | 1.000 | 1.000 | 1.000 | 1.093 | 1.000 | 2.000 |
| AS_em_kars | 2.500 | 7.500 | 7.500 | 8.605 | 7.500 | 60.000 |
| AS_karspit | 3.310 | 5.940 | 5.940 | 6.195 | 5.940 | 20.000 |
| AS_karslev | 2.670 | 4.800 | 4.800 | 4.807 | 4.800 | 7.000 |
| AS_meluton | 0.000 | 1.000 | 1.000 | 0.907 | 1.000 | 1.000 |
| AS_haittael_ei | 0.000 | 1.000 | 1.000 | 0.860 | 1.000 | 1.000 |
| AS_haittael_laatu | 1.000 | 1.000 | 1.000 | 2.070 | 4.000 | 4.000 |
| AS_ilma_aistin | 0.000 | 0.000 | 0.000 | 0.186 | 0.000 | 1.000 |
| AS_ilma_amm | 0.000 | 0.000 | 0.000 | 0.186 | 0.000 | 1.000 |
| AS_ilma_pöly | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| AS_ilma_muu | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| AS_kosteus | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| AS_valaistus | 0.000 | 0.000 | 0.000 | 0.047 | 0.000 | 1.000 |
| AS_alusta12345 | 1.000 | 1.000 | 1.000 | 2.791 | 1.000 | 12.000 |
| AS_alusta_5_laatu | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| AS_latt_rakenne1234 | 12.000 | 13.000 | 13.000 | 12.837 | 13.000 | 13.000 |
| AS_pr_ritila | 0.000 | 0.000 | 0.000 | 4.279 | 0.000 | 41.000 |
| AS_pr_viemar | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| AS_kuiv_mat12345 | 1.000 | 1.500 | 1.500 | 2.012 | 1.500 | 14.000 |
| AS_kuiv_5_mika | 0.000 | 3.000 | 3.000 | 2.884 | 3.000 | 4.000 |
| AS_maara1234 | 0.000 | 4.000 | 4.000 | 3.698 | 4.000 | 4.000 |
| AS_tonkimat123456 | 1.000 | 1.000 | 1.000 | 1.349 | 1.000 | 12.000 |
| AS_tonkimat_6_mika | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| AS_mat_vaiht | 0.000 | 1.000 | 1.000 | 0.953 | 1.000 | 1.000 |
| AS_maara123 | 0.000 | 2.000 | 2.000 | 2.070 | 2.000 | 3.000 |
| AS_annostelu1234 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 3.000 |
| AS_lannanpoisto12 | 0.000 | 2.000 | 2.000 | 2.116 | 2.000 | 12.000 |
| AS_rak_kunto | 0.000 | 0.000 | 0.000 | 0.023 | 0.000 | 1.000 |
| AS_latt_pitava | 0.000 | 0.000 | 0.000 | 0.023 | 0.000 | 1.000 |
| AS_sairkars | 0.000 | 0.000 | 0.000 | 0.256 | 0.500 | 1.000 |
| AS_sk_parempi | 0.000 | 1.000 | 1.000 | 0.849 | 1.000 | 1.000 |
| AS_sk_kiintea | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| AS_sk_kuivike | 0.000 | 0.000 | 0.000 | 0.093 | 0.000 | 1.000 |
| AS_sk_siisti | 0.000 | 0.000 | 0.000 | 0.070 | 0.000 | 1.000 |
| AS_sk_kuiva | 0.000 | 0.000 | 0.000 | 0.035 | 0.000 | 1.000 |
| AS_sk_syörauha | 0.000 | 0.000 | 0.000 | 0.116 | 0.000 | 1.000 |
| AS_sk_juorauha | 0.000 | 0.000 | 0.000 | 0.116 | 0.000 | 1.000 |
| AS_ruoklaite12345 | 0.000 | 4.000 | 4.000 | 3.814 | 4.000 | 4.000 |
| AS_ruokpaikka | 0.000 | 1.000 | 1.000 | 1.047 | 1.000 | 4.000 |
| AS_ruokpuht | 0.000 | 0.000 | 0.000 | 0.140 | 0.000 | 1.000 |
| AS_juomalaite123 | 0.000 | 1.000 | 1.000 | 0.977 | 1.000 | 1.000 |
| AS_juonalkm | 0.222 | 1.000 | 1.000 | 1.011 | 1.000 | 2.250 |
| AS_juomapuht | 0.000 | 0.000 | 0.000 | 0.023 | 0.000 | 1.000 |
| AS_juomatoim | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| AS_rauhallisuus123 | 0.000 | 1.000 | 1.000 | 0.953 | 1.000 | 1.000 |
| AS_hoitotarveKE | 1.000 | 1.000 | 1.000 | 1.442 | 2.000 | 2.000 |
| AS_stereo | 0.000 | 0.000 | 0.000 | 0.140 | 0.000 | 1.000 |
| TII_1ast_jout_samassa_2asteiole | 0.000 | 0.000 | 0.000 | 0.186 | 0.000 | 2.000 |
| TII_valiseinat | 0.000 | 0.000 | 0.000 | 0.488 | 0.000 | 16.000 |
| TII_meluton | 0.000 | 1.000 | 1.000 | 0.791 | 1.000 | 1.000 |
| TII_haittael_ei | 0.000 | 1.000 | 1.000 | 0.767 | 1.000 | 1.000 |
| TII_ilma_aistin | 0.000 | 0.000 | 0.000 | 0.116 | 0.000 | 1.000 |
| TII_ilma_amm | 0.000 | 0.000 | 0.000 | 0.140 | 0.000 | 1.000 |
| TII_ilma_pöly | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| TII_ilma_muu | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| TII_kosteus | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| TII_valaistus | 0.000 | 0.000 | 0.000 | 0.023 | 0.000 | 1.000 |
| TII_alusta12345 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
| TII_latt_rakenne1234 | 1.000 | 13.000 | 13.000 | 11.744 | 13.000 | 23.000 |
| TII_pr_ritila | 0.000 | 0.000 | 0.000 | 4.140 | 0.000 | 50.000 |
| TII_pr_viemar | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| TII_kuiv_mat12345 | 1.000 | 2.000 | 2.000 | 3.721 | 2.000 | 15.000 |
| TII_kuiv_5_mika | 1.000 | 1.000 | 1.000 | 1.023 | 1.000 | 2.000 |
| TII_maara1234 | 1.000 | 3.000 | 3.000 | 3.349 | 3.000 | 23.000 |
| TII_tonkimat_6_mika | 1.000 | 1.000 | 1.000 | 1.326 | 1.000 | 5.000 |
| TII_lelu1234 | 2.000 | 4.000 | 4.000 | 4.395 | 4.000 | 24.000 |
| TII_mat_vaiht | 0.000 | 1.000 | 1.000 | 0.977 | 1.000 | 1.000 |
| TII_maara123 | 1.000 | 2.000 | 2.000 | 1.930 | 2.000 | 3.000 |
| TII_annostelu1234 | 1.000 | 1.000 | 1.000 | 1.163 | 1.000 | 4.000 |
| TII_lannanpoisto12 | 1.000 | 1.000 | 1.000 | 2.535 | 5.000 | 5.000 |
| TII_rak_kunto | 0.000 | 0.000 | 0.000 | 0.047 | 0.000 | 1.000 |
| TII_latt_pitava | 0.000 | 0.000 | 0.000 | 0.070 | 0.000 | 1.000 |
| TII_sairkars | 0.000 | 1.000 | 1.000 | 0.907 | 1.000 | 1.000 |
| TII_ruok_0nonlock_1lock | 0.000 | 0.000 | 0.000 | 0.395 | 1.000 | 1.000 |
| TII_ruokpuht | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| TII_juomalaite123 | 1.000 | 1.000 | 1.000 | 1.279 | 1.000 | 12.000 |
| TII_juomapuht | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| TII_juomatoim | 0.000 | 0.000 | 0.000 | 0.047 | 0.000 | 2.000 |
| TII_rauhallisuus123 | 1.000 | 1.000 | 1.000 | 1.023 | 1.000 | 2.000 |
| TII_hoitotarveKE | 1.000 | 1.000 | 2.000 | 1.512 | 2.000 | 2.000 |
| TII_stereo | 0.000 | 0.000 | 0.000 | 0.093 | 0.000 | 1.000 |
| POR_meluton | 0.000 | 1.000 | 1.000 | 0.767 | 1.000 | 1.000 |
| POR_haittael_ei | 0.000 | 1.000 | 1.000 | 0.860 | 1.000 | 1.000 |
| POR_haittael_laatu | 1.000 | 1.000 | 1.000 | 2.186 | 4.000 | 4.000 |
| POR_ilma_aistin | 0.000 | 0.000 | 0.000 | 0.023 | 0.000 | 1.000 |
| POR_ilma_amm | 0.000 | 0.000 | 0.000 | 0.023 | 0.000 | 1.000 |
| POR_ilma_pöly | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| POR_ilma_muu | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| POR_kosteus | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| POR_valaistus | 0.000 | 0.000 | 0.000 | 0.058 | 0.000 | 1.000 |
| POR_latt_rakenne1234 | 1.000 | 12.000 | 12.000 | 13.395 | 12.000 | 123.000 |
| POR_pr_rako | 0.000 | 0.000 | 0.000 | 0.884 | 0.000 | 38.000 |
| POR_maara1234 | 2.000 | 3.000 | 3.000 | 2.953 | 3.000 | 4.000 |
| POR_tonkimat_6_mika | 1.000 | 1.000 | 1.000 | 1.442 | 1.000 | 5.000 |
| POR_lelu1234 | 2.000 | 4.000 | 4.000 | 3.930 | 4.000 | 5.000 |
| POR_lelukomm | 1.000 | 1.000 | 1.000 | 1.140 | 1.000 | 4.000 |
| POR_mat_vaiht | 1.000 | 1.000 | 1.000 | 1.023 | 1.000 | 2.000 |
| POR_maara123 | 1.000 | 2.000 | 2.000 | 2.000 | 2.000 | 3.000 |
| POR_annostelu1234 | 1.000 | 1.000 | 1.000 | 1.233 | 1.000 | 4.000 |
| POR_lannanpoisto12 | 1.000 | 2.000 | 2.000 | 1.907 | 2.000 | 2.000 |
| POR_rak_kunto | 0.000 | 0.000 | 0.000 | 0.070 | 0.000 | 1.000 |
| POR_latt_pitava | 0.000 | 0.000 | 0.000 | 0.070 | 0.000 | 1.000 |
| POR_sairkars | 1.000 | 1.000 | 1.000 | 1.860 | 3.000 | 5.000 |
| POR_ruoklaite12345 | 2.000 | 2.500 | 2.500 | 3.012 | 2.500 | 25.000 |
| POR_ruokpaikka | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
| POR_ruokpuht | 0.000 | 0.000 | 0.000 | 0.070 | 0.000 | 1.000 |
| POR_juomalaite123 | 1.000 | 1.000 | 1.000 | 1.279 | 1.000 | 13.000 |
| POR_juonalkm | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
| POR_juomapuht | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| POR_juomatoim | 0.000 | 0.000 | 0.000 | 0.023 | 0.000 | 1.000 |
| POR_rauhallisuus123 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
| Hajukarjut_per_emakko | 0.000 | 0.010 | 0.010 | 0.013 | 0.015 | 0.060 |
| TII_VIRMaa_0_ei_1pellel_2pelvir_3niukuihiemnvir_4riirunkuiv | 0.000 | 2.000 | 3.000 | 2.884 | 4.000 | 4.000 |
| TII_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme | 0.000 | 1.000 | 2.000 | 1.884 | 3.000 | 3.000 |
| AS_VIRMaa_0ei_1pellel_2pelvir_3niukuihiemvir_4riirunkuiv | 0.000 | 1.000 | 2.000 | 2.047 | 3.000 | 4.000 |
| AS_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme | 0.000 | 1.000 | 1.000 | 1.442 | 2.000 | 3.000 |
| POR_VIRMaa_0_ei_1pellel_2pelvir_3niukui_4riikuiv | 0.000 | 2.000 | 3.000 | 2.698 | 3.500 | 4.000 |
| POR_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme | 0.000 | 1.500 | 2.000 | 1.791 | 2.000 | 3.000 |
| Koulmax_1peru_2ops_3a_4amk_5yl | 2.000 | 3.000 | 3.000 | 3.209 | 3.000 | 5.000 |
| Stressi_1erpal_4jnkv | 1.000 | 2.000 | 3.000 | 2.884 | 4.000 | 4.000 |
| EMKUOLLJAKO | 0.000 | 0.000 | 0.000 | 0.465 | 1.000 | 1.000 |
| EMPOISJAKO | 0.000 | 0.000 | 0.000 | 0.442 | 1.000 | 1.000 |
| EMENKUOLLJAKO | 0.000 | 0.000 | 0.000 | 0.419 | 1.000 | 1.000 |
| EMENPOISJAKO | 0.000 | 0.000 | 0.000 | 0.372 | 1.000 | 1.000 |
| NIVEL_01 | 1.000 | 1.000 | 1.000 | 1.419 | 2.000 | 2.000 |
| PAISE_01 | 1.000 | 1.000 | 1.000 | 1.419 | 2.000 | 2.000 |
| MAKUU01 | 1.000 | 1.000 | 1.000 | 1.419 | 2.000 | 2.000 |
| KOKO_01 | 1.000 | 1.000 | 1.000 | 1.419 | 2.000 | 2.000 |
| OSA_01 | 1.000 | 1.000 | 1.000 | 1.419 | 2.000 | 2.000 |
| JOKUHYLK_01 | 1.000 | 1.000 | 1.000 | 1.419 | 2.000 | 2.000 |
| PLEUR_01 | 0.000 | 0.000 | 0.000 | 0.279 | 1.000 | 1.000 |
| PNEUM_01 | 1.000 | 1.000 | 1.000 | 1.419 | 2.000 | 2.000 |
| SAIRKARS_AST_TII | 0.000 | 1.000 | 1.000 | 0.767 | 1.000 | 1.000 |
KreateTableOne = function(x, ...){
t1 = tableone::CreateTableOne(data=x, ...)
t2 = print(t1, quote=TRUE)
rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
return(t2)
}Yhteenveto kuolleisuuden mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
tilatkat<-tilat[,1:218]%>%mutate_all(as.factor)
tilatkat$EMKUOL<-tilat$EMKUOLLJAKO
table1 = KreateTableOne(x=tilatkat, strata='EMKUOL')table1%>%
kable("html", align = "rrr", caption = "Data variable summary strat by EMKUOL") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 23 | 20 | ||
| Haastrooli_1OmEiosall_2OmOsall_3Esimies (%) | 0.431 | |||
| 1 | 4 ( 17.4) | 6 ( 30.0) | ||
| 2 | 17 ( 73.9) | 11 ( 55.0) | ||
| 3 | 2 ( 8.7) | 3 ( 15.0) | ||
| Tuotsuunta = 2 (%) | 14 ( 60.9) | 7 ( 35.0) | 0.165 | |
| Karjut_astsiem (%) | 0.264 | |||
| 0 | 21 ( 91.3) | 17 ( 85.0) | ||
| 1 | 1 ( 4.3) | 0 ( 0.0) | ||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.0) | ||
| 6 | 0 ( 0.0) | 2 ( 10.0) | ||
| Tautsu = 1 (%) | 15 ( 65.2) | 15 ( 75.0) | 0.716 | |
| Tautsuok = 1 (%) | 9 ( 39.1) | 12 ( 60.0) | 0.289 | |
| Tautsu_012 (%) | 0.733 | |||
| 0 | 8 ( 34.8) | 5 ( 25.0) | ||
| 1 | 7 ( 30.4) | 6 ( 30.0) | ||
| 2 | 8 ( 34.8) | 9 ( 45.0) | ||
| Siilotkat = 1 (%) | 21 ( 91.3) | 18 ( 90.0) | 1.000 | |
| Tuhoei = 1 (%) | 16 ( 69.6) | 19 ( 95.0) | 0.081 | |
| Eikulkuih = 1 (%) | 16 ( 69.6) | 11 ( 55.0) | 0.503 | |
| Eikulkuel = 1 (%) | 14 ( 60.9) | 16 ( 80.0) | 0.303 | |
| Suojvar = 1 (%) | 23 (100.0) | 20 (100.0) | NA | |
| Suojvarpuh = 1 (%) | 23 (100.0) | 19 ( 95.0) | 0.944 | |
| Kadetpesu = 1 (%) | 14 ( 60.9) | 16 ( 80.0) | 0.303 | |
| Toimsiis = 1 (%) | 22 ( 95.7) | 19 ( 95.0) | 1.000 | |
| Saappesu = 1 (%) | 14 ( 60.9) | 17 ( 85.0) | 0.156 | |
| Lasthu = 1 (%) | 19 ( 82.6) | 17 ( 85.0) | 1.000 | |
| Teurkuski_0paaseesikalaan_1eipaase = 1 (%) | 17 ( 73.9) | 13 ( 65.0) | 0.763 | |
| JOU_kertayt_0ei = 1 (%) | 3 ( 13.0) | 4 ( 20.0) | 0.840 | |
| JOU_tuotvaiherill_0ei = 1 (%) | 16 ( 69.6) | 15 ( 75.0) | 0.956 | |
| JOU_pesu_0ei = 1 (%) | 4 ( 17.4) | 2 ( 10.0) | 0.798 | |
| JOU_pesuaine_0ei = 1 (%) | 2 ( 8.7) | 2 ( 10.0) | 1.000 | |
| JOU_desinf_liu_0ei_1liuos_2kuiva (%) | 0.406 | |||
| 0 | 20 ( 87.0) | 18 ( 90.0) | ||
| 1 | 0 ( 0.0) | 1 ( 5.0) | ||
| 2 | 2 ( 8.7) | 0 ( 0.0) | ||
| 12 | 1 ( 4.3) | 1 ( 5.0) | ||
| JOU_tyhjana_mi1vrk_0ei = 1 (%) | 5 ( 21.7) | 8 ( 40.0) | 0.333 | |
| PORSOSASTO_kertayt_0ei = 1 (%) | 9 ( 39.1) | 9 ( 45.0) | 0.937 | |
| PORS_tuotvaiherill_0ei = 1 (%) | 19 ( 82.6) | 14 ( 70.0) | 0.539 | |
| PORS_pesu_0ei = 1 (%) | 17 ( 73.9) | 16 ( 80.0) | 0.913 | |
| PORS_pesuaine_0ei = 1 (%) | 4 ( 17.4) | 6 ( 30.0) | 0.539 | |
| PORS_desinf_0ei_1LIU_2KUIVA (%) | 0.623 | |||
| 0 | 5 ( 21.7) | 4 ( 20.0) | ||
| 1 | 9 ( 39.1) | 10 ( 50.0) | ||
| 2 | 7 ( 30.4) | 3 ( 15.0) | ||
| 12 | 2 ( 8.7) | 3 ( 15.0) | ||
| PORS_tyhjana_mi1vr_0ei = 1 (%) | 13 ( 56.5) | 13 ( 65.0) | 0.799 | |
| Raa_0ei_1kontti_2huone (%) | 0.456 | |||
| 0 | 2 ( 8.7) | 1 ( 5.0) | ||
| 1 | 19 ( 82.6) | 15 ( 75.0) | ||
| 2 | 0 ( 0.0) | 2 ( 10.0) | ||
| 12 | 2 ( 8.7) | 2 ( 10.0) | ||
| Raa_auto_hakee_0ei = 1 (%) | 16 ( 69.6) | 11 ( 55.0) | 0.503 | |
| Raa_viilea_0ei = 1 (%) | 21 ( 91.3) | 17 ( 85.0) | 0.868 | |
| Raa_tuhoelain_1eipaase_0paaseesic = 1 (%) | 13 ( 56.5) | 13 ( 65.0) | 0.799 | |
| Tuhoelmerkkeja_0kylla_1ei = 1 (%) | 5 ( 21.7) | 5 ( 25.0) | 1.000 | |
| Lintuja_0kylla_1ei = 1 (%) | 19 ( 82.6) | 14 ( 70.0) | 0.539 | |
| Tuho_ohjelma = 1 (%) | 2 ( 8.7) | 3 ( 15.0) | 0.868 | |
| kissoja0on1ei (%) | 0.005 | |||
| 0 | 19 ( 82.6) | 7 ( 35.0) | ||
| 0.5 | 0 ( 0.0) | 2 ( 10.0) | ||
| 1 | 4 ( 17.4) | 11 ( 55.0) | ||
| Kotielain_sikalaan_0kylla_1ei = 1 (%) | 17 ( 73.9) | 17 ( 85.0) | 0.606 | |
| Vesi_1kunn_0oma = 1 (%) | 16 ( 69.6) | 11 ( 55.0) | 0.503 | |
| Ery = 1 (%) | 23 (100.0) | 20 (100.0) | NA | |
| Parvo = 1 (%) | 23 (100.0) | 20 (100.0) | NA | |
| Koli = 1 (%) | 22 ( 95.7) | 19 ( 95.0) | 1.000 | |
| Sirko = 1 (%) | 8 ( 34.8) | 5 ( 25.0) | 0.716 | |
| ClC = 1 (%) | 1 ( 4.3) | 2 ( 10.0) | 0.900 | |
| ClA = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| SI = 1 (%) | 1 ( 4.3) | 3 ( 15.0) | 0.501 | |
| APP = 1 (%) | 3 ( 13.0) | 2 ( 10.0) | 1.000 | |
| Loisaika_1ennenpors_2_porskars = 2 (%) | 9 ( 39.1) | 7 ( 35.0) | 1.000 | |
| Uusiryh (%) | 0.524 | |||
| 1 | 1 ( 4.3) | 2 ( 10.0) | ||
| 2 | 20 ( 87.0) | 18 ( 90.0) | ||
| 3 | 1 ( 4.3) | 0 ( 0.0) | ||
| 4 | 1 ( 4.3) | 0 ( 0.0) | ||
| Ton_tiheys_1aina_2jaetaan = 2 (%) | 4 ( 17.4) | 0 ( 0.0) | 0.152 | |
| Yhdistaggrtmp_1eiongelma_2tmp_3eitmp (%) | 0.334 | |||
| 1 | 4 ( 17.4) | 1 ( 5.0) | ||
| 2 | 9 ( 39.1) | 13 ( 65.0) | ||
| 3 | 4 ( 17.4) | 2 ( 10.0) | ||
| 12 | 6 ( 26.1) | 4 ( 20.0) | ||
| Muutelkaynn_0ei_1_satunn_2kaynnmuusaann (%) | 0.142 | |||
| 0 | 10 ( 43.5) | 6 ( 30.0) | ||
| 1 | 12 ( 52.2) | 9 ( 45.0) | ||
| 2 | 1 ( 4.3) | 5 ( 25.0) | ||
| maitokuume = 1 (%) | 12 ( 52.2) | 10 ( 50.0) | 1.000 | |
| metriitti = 1 (%) | 10 ( 43.5) | 9 ( 45.0) | 1.000 | |
| valuttelu = 1 (%) | 2 ( 8.7) | 3 ( 15.0) | 0.868 | |
| mastiitti = 1 (%) | 4 ( 17.4) | 6 ( 30.0) | 0.539 | |
| ontuma = 1 (%) | 15 ( 65.2) | 16 ( 80.0) | 0.461 | |
| syomattomyys = 1 (%) | 10 ( 43.5) | 12 ( 60.0) | 0.438 | |
| kuume = 1 (%) | 2 ( 8.7) | 4 ( 20.0) | 0.531 | |
| loukkaantuminen = 1 (%) | 10 ( 43.5) | 6 ( 30.0) | 0.551 | |
| AB_rutiinilaak = 1 (%) | 2 ( 8.7) | 4 ( 20.0) | 0.531 | |
| Oksitosiini_rutiinisti = 1 (%) | 8 ( 34.8) | 9 ( 45.0) | 0.711 | |
| Kaynnistys_rutiinisti = 1 (%) | 0 ( 0.0) | 4 ( 20.0) | 0.084 | |
| NSAID_porsituksessa_rutiini = 1 (%) | 6 ( 26.1) | 4 ( 20.0) | 0.913 | |
| OMATENSIKOT_0EI_1KYLLa = 1 (%) | 15 ( 65.2) | 13 ( 65.0) | 1.000 | |
| Ensikk_valisiirtkars_ennensiem = 1 (%) | 8 ( 34.8) | 9 ( 45.0) | 0.711 | |
| Ensikk_kiihruok = 1 (%) | 8 ( 34.8) | 8 ( 40.0) | 0.971 | |
| Ensikk_karjukontaktiensi_0hajutainako_1aidanlapi_2kars (%) | 0.401 | |||
| 0 | 2 ( 8.7) | 2 ( 10.0) | ||
| 1 | 19 ( 82.6) | 18 ( 90.0) | ||
| 2 | 2 ( 8.7) | 0 ( 0.0) | ||
| siemika (%) | 0.208 | |||
| 7 | 1 ( 4.3) | 0 ( 0.0) | ||
| 7.5 | 1 ( 4.3) | 1 ( 5.0) | ||
| 8 | 20 ( 87.0) | 14 ( 70.0) | ||
| 8.5 | 0 ( 0.0) | 4 ( 20.0) | ||
| 9.5 | 1 ( 4.3) | 1 ( 5.0) | ||
| Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk (%) | 0.386 | |||
| 1 | 1 ( 4.3) | 0 ( 0.0) | ||
| 2 | 3 ( 13.0) | 2 ( 10.0) | ||
| 3 | 17 ( 73.9) | 18 ( 90.0) | ||
| 4 | 2 ( 8.7) | 0 ( 0.0) | ||
| Kiimantark_ryhmakaytt = 1 (%) | 20 ( 87.0) | 18 ( 90.0) | 1.000 | |
| Kiimantarkalkaa_vrkvier (%) | 0.264 | |||
| 0 | 7 ( 30.4) | 5 ( 25.0) | ||
| 1 | 14 ( 60.9) | 9 ( 45.0) | ||
| 3 | 0 ( 0.0) | 3 ( 15.0) | ||
| 4 | 0 ( 0.0) | 1 ( 5.0) | ||
| 5 | 2 ( 8.7) | 2 ( 10.0) | ||
| Kiimamerk_emakonselka = 1 (%) | 17 ( 73.9) | 20 (100.0) | 0.043 | |
| Kiimantark_postsiem = 1 (%) | 21 ( 91.3) | 20 (100.0) | 0.532 | |
| Postsiem_ryhmakaytt_havainnointi = 1 (%) | 20 ( 87.0) | 18 ( 90.0) | 1.000 | |
| Tiin_ultra2 (%) | 0.364 | |||
| 6 | 22 ( 95.7) | 19 ( 95.0) | ||
| 8 | 1 ( 4.3) | 0 ( 0.0) | ||
| 10 | 0 ( 0.0) | 1 ( 5.0) | ||
| Tiin_ultra_1yhdesti_2kahdesti (%) | 0.533 | |||
| 0 | 5 ( 21.7) | 2 ( 10.0) | ||
| 1 | 15 ( 65.2) | 14 ( 70.0) | ||
| 2 | 3 ( 13.0) | 4 ( 20.0) | ||
| Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen (%) | 0.115 | |||
| 0 | 10 ( 43.5) | 6 ( 30.0) | ||
| 1 | 0 ( 0.0) | 2 ( 10.0) | ||
| 2 | 2 ( 8.7) | 6 ( 30.0) | ||
| 3 | 2 ( 8.7) | 0 ( 0.0) | ||
| 4 | 9 ( 39.1) | 6 ( 30.0) | ||
| Pesantekomatmaara_1runsas_2jnkv_3niukka (%) | 0.763 | |||
| 1 | 1 ( 4.3) | 2 ( 10.0) | ||
| 2 | 18 ( 78.3) | 15 ( 75.0) | ||
| 3 | 4 ( 17.4) | 3 ( 15.0) | ||
| Sisatutk_ennenoksitos = 1 (%) | 7 ( 30.4) | 8 ( 40.0) | 0.737 | |
| Porsitusaputekn_1empesu_2kaspesu_3kasine_4liukaste (%) | 0.570 | |||
| 34 | 8 ( 34.8) | 7 ( 35.0) | ||
| 124 | 2 ( 8.7) | 0 ( 0.0) | ||
| 134 | 8 ( 34.8) | 9 ( 45.0) | ||
| 234 | 1 ( 4.3) | 0 ( 0.0) | ||
| 1234 | 4 ( 17.4) | 4 ( 20.0) | ||
| PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa = 2 (%) | 19 ( 82.6) | 16 ( 80.0) | 1.000 | |
| Ruoksu_0ei_1itse_2neuvoja_3kyllaeitietoa (%) | 0.897 | |||
| 1 | 1 ( 4.3) | 1 ( 5.0) | ||
| 2 | 19 ( 82.6) | 15 ( 75.0) | ||
| 3 | 1 ( 4.3) | 2 ( 10.0) | ||
| 12 | 2 ( 8.7) | 2 ( 10.0) | ||
| Yksilöll_ruokinta = 1 (%) | 17 ( 73.9) | 14 ( 70.0) | 1.000 | |
| AS_1ast_jout_samassa_2asteiole = 2 (%) | 18 ( 78.3) | 18 ( 90.0) | 0.531 | |
| AS_er_os_lkm = 2 (%) | 2 ( 8.7) | 2 ( 10.0) | 1.000 | |
| AS_em_kars (%) | 0.400 | |||
| 2.5 | 1 ( 4.3) | 0 ( 0.0) | ||
| 7 | 0 ( 0.0) | 1 ( 5.0) | ||
| 7.5 | 21 ( 91.3) | 18 ( 90.0) | ||
| 8 | 0 ( 0.0) | 1 ( 5.0) | ||
| 60 | 1 ( 4.3) | 0 ( 0.0) | ||
| AS_karspit (%) | 0.429 | |||
| 3.31 | 1 ( 4.3) | 0 ( 0.0) | ||
| 4.4 | 1 ( 4.3) | 0 ( 0.0) | ||
| 5.94 | 20 ( 87.0) | 19 ( 95.0) | ||
| 7 | 0 ( 0.0) | 1 ( 5.0) | ||
| 20 | 1 ( 4.3) | 0 ( 0.0) | ||
| AS_karslev (%) | 0.429 | |||
| 2.67 | 1 ( 4.3) | 0 ( 0.0) | ||
| 3.02 | 0 ( 0.0) | 1 ( 5.0) | ||
| 4.8 | 20 ( 87.0) | 19 ( 95.0) | ||
| 6.8 | 1 ( 4.3) | 0 ( 0.0) | ||
| 7 | 1 ( 4.3) | 0 ( 0.0) | ||
| AS_meluton = 1 (%) | 21 ( 91.3) | 18 ( 90.0) | 1.000 | |
| AS_haittael_ei = 1 (%) | 20 ( 87.0) | 17 ( 85.0) | 1.000 | |
| AS_haittael_laatu (%) | 0.637 | |||
| 1 | 15 ( 65.2) | 11 ( 55.0) | ||
| 2 | 1 ( 4.3) | 1 ( 5.0) | ||
| 3 | 1 ( 4.3) | 0 ( 0.0) | ||
| 4 | 6 ( 26.1) | 8 ( 40.0) | ||
| AS_ilma_aistin = 1 (%) | 3 ( 13.0) | 5 ( 25.0) | 0.540 | |
| AS_ilma_amm = 1 (%) | 3 ( 13.0) | 5 ( 25.0) | 0.540 | |
| AS_ilma_pöly = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| AS_ilma_muu = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| AS_kosteus = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| AS_valaistus = 1 (%) | 1 ( 4.3) | 1 ( 5.0) | 1.000 | |
| AS_alusta12345 = 12 (%) | 2 ( 8.7) | 5 ( 25.0) | 0.303 | |
| AS_alusta_5_laatu = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| AS_latt_rakenne1234 = 13 (%) | 21 ( 91.3) | 15 ( 75.0) | 0.303 | |
| AS_pr_ritila (%) | 0.535 | |||
| 0 | 21 ( 91.3) | 15 ( 75.0) | ||
| 20 | 1 ( 4.3) | 2 ( 10.0) | ||
| 25 | 1 ( 4.3) | 1 ( 5.0) | ||
| 33 | 0 ( 0.0) | 1 ( 5.0) | ||
| 41 | 0 ( 0.0) | 1 ( 5.0) | ||
| AS_pr_viemar = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| AS_kuiv_mat12345 (%) | 0.397 | |||
| 1 | 2 ( 8.7) | 2 ( 10.0) | ||
| 1.5 | 19 ( 82.6) | 16 ( 80.0) | ||
| 2 | 0 ( 0.0) | 2 ( 10.0) | ||
| 12 | 1 ( 4.3) | 0 ( 0.0) | ||
| 14 | 1 ( 4.3) | 0 ( 0.0) | ||
| AS_kuiv_5_mika (%) | 0.157 | |||
| 0 | 0 ( 0.0) | 2 ( 10.0) | ||
| 3 | 23 (100.0) | 17 ( 85.0) | ||
| 4 | 0 ( 0.0) | 1 ( 5.0) | ||
| AS_maara1234 (%) | 0.372 | |||
| 0 | 1 ( 4.3) | 0 ( 0.0) | ||
| 1 | 1 ( 4.3) | 0 ( 0.0) | ||
| 2 | 0 ( 0.0) | 1 ( 5.0) | ||
| 3 | 1 ( 4.3) | 3 ( 15.0) | ||
| 4 | 20 ( 87.0) | 16 ( 80.0) | ||
| AS_tonkimat123456 (%) | 0.364 | |||
| 1 | 22 ( 95.7) | 19 ( 95.0) | ||
| 5 | 0 ( 0.0) | 1 ( 5.0) | ||
| 12 | 1 ( 4.3) | 0 ( 0.0) | ||
| AS_tonkimat_6_mika = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| AS_mat_vaiht = 1 (%) | 22 ( 95.7) | 19 ( 95.0) | 1.000 | |
| AS_maara123 (%) | 0.054 | |||
| 0 | 0 ( 0.0) | 1 ( 5.0) | ||
| 2 | 18 ( 78.3) | 19 ( 95.0) | ||
| 3 | 5 ( 21.7) | 0 ( 0.0) | ||
| AS_annostelu1234 (%) | 0.639 | |||
| 0 | 1 ( 4.3) | 1 ( 5.0) | ||
| 1 | 21 ( 91.3) | 19 ( 95.0) | ||
| 3 | 1 ( 4.3) | 0 ( 0.0) | ||
| AS_lannanpoisto12 (%) | 0.524 | |||
| 0 | 1 ( 4.3) | 0 ( 0.0) | ||
| 1 | 1 ( 4.3) | 2 ( 10.0) | ||
| 2 | 20 ( 87.0) | 18 ( 90.0) | ||
| 12 | 1 ( 4.3) | 0 ( 0.0) | ||
| AS_rak_kunto = 1 (%) | 0 ( 0.0) | 1 ( 5.0) | 0.944 | |
| AS_latt_pitava = 1 (%) | 1 ( 4.3) | 0 ( 0.0) | 1.000 | |
| AS_sairkars = 1 (%) | 3 ( 13.0) | 8 ( 40.0) | 0.095 | |
| AS_sk_parempi (%) | 0.538 | |||
| 0 | 3 ( 13.0) | 3 ( 15.0) | ||
| 0.5 | 0 ( 0.0) | 1 ( 5.0) | ||
| 1 | 20 ( 87.0) | 16 ( 80.0) | ||
| AS_sk_kiintea = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| AS_sk_kuivike (%) | 0.891 | |||
| 0 | 20 ( 87.0) | 18 ( 90.0) | ||
| 0.5 | 1 ( 4.3) | 1 ( 5.0) | ||
| 1 | 2 ( 8.7) | 1 ( 5.0) | ||
| AS_sk_siisti = 1 (%) | 2 ( 8.7) | 1 ( 5.0) | 1.000 | |
| AS_sk_kuiva (%) | 0.364 | |||
| 0 | 22 ( 95.7) | 19 ( 95.0) | ||
| 0.5 | 0 ( 0.0) | 1 ( 5.0) | ||
| 1 | 1 ( 4.3) | 0 ( 0.0) | ||
| AS_sk_syörauha = 1 (%) | 2 ( 8.7) | 3 ( 15.0) | 0.868 | |
| AS_sk_juorauha = 1 (%) | 2 ( 8.7) | 3 ( 15.0) | 0.868 | |
| AS_ruoklaite12345 = 4 (%) | 22 ( 95.7) | 19 ( 95.0) | 1.000 | |
| AS_ruokpaikka (%) | 0.402 | |||
| 0 | 1 ( 4.3) | 0 ( 0.0) | ||
| 1 | 21 ( 91.3) | 20 (100.0) | ||
| 4 | 1 ( 4.3) | 0 ( 0.0) | ||
| AS_ruokpuht = 1 (%) | 2 ( 8.7) | 4 ( 20.0) | 0.531 | |
| AS_juomalaite123 = 1 (%) | 23 (100.0) | 19 ( 95.0) | 0.944 | |
| AS_juonalkm (%) | 0.364 | |||
| 0.222222222222222 | 0 ( 0.0) | 1 ( 5.0) | ||
| 1 | 22 ( 95.7) | 19 ( 95.0) | ||
| 2.25 | 1 ( 4.3) | 0 ( 0.0) | ||
| AS_juomapuht = 1 (%) | 0 ( 0.0) | 1 ( 5.0) | 0.944 | |
| AS_juomatoim = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| AS_rauhallisuus123 = 1 (%) | 22 ( 95.7) | 19 ( 95.0) | 1.000 | |
| AS_hoitotarveKE = 2 (%) | 9 ( 39.1) | 10 ( 50.0) | 0.683 | |
| AS_stereo = 1 (%) | 4 ( 17.4) | 2 ( 10.0) | 0.798 | |
| TII_1ast_jout_samassa_2asteiole (%) | 0.401 | |||
| 0 | 19 ( 82.6) | 18 ( 90.0) | ||
| 1 | 2 ( 8.7) | 2 ( 10.0) | ||
| 2 | 2 ( 8.7) | 0 ( 0.0) | ||
| TII_valiseinat (%) | 0.440 | |||
| 0 | 22 ( 95.7) | 16 ( 80.0) | ||
| 0.5 | 0 ( 0.0) | 1 ( 5.0) | ||
| 1 | 1 ( 4.3) | 1 ( 5.0) | ||
| 2.5 | 0 ( 0.0) | 1 ( 5.0) | ||
| 16 | 0 ( 0.0) | 1 ( 5.0) | ||
| TII_meluton = 1 (%) | 18 ( 78.3) | 16 ( 80.0) | 1.000 | |
| TII_haittael_ei = 1 (%) | 19 ( 82.6) | 14 ( 70.0) | 0.539 | |
| TII_ilma_aistin = 1 (%) | 1 ( 4.3) | 4 ( 20.0) | 0.263 | |
| TII_ilma_amm = 1 (%) | 1 ( 4.3) | 5 ( 25.0) | 0.131 | |
| TII_ilma_pöly = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| TII_ilma_muu = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| TII_kosteus = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| TII_valaistus = 1 (%) | 1 ( 4.3) | 0 ( 0.0) | 1.000 | |
| TII_alusta12345 = 1 (%) | 23 (100.0) | 20 (100.0) | NA | |
| TII_latt_rakenne1234 (%) | 0.624 | |||
| 1 | 2 ( 8.7) | 3 ( 15.0) | ||
| 12 | 2 ( 8.7) | 2 ( 10.0) | ||
| 13 | 19 ( 82.6) | 14 ( 70.0) | ||
| 23 | 0 ( 0.0) | 1 ( 5.0) | ||
| TII_pr_ritila (%) | 0.217 | |||
| 0 | 22 ( 95.7) | 16 ( 80.0) | ||
| 20 | 1 ( 4.3) | 0 ( 0.0) | ||
| 28 | 0 ( 0.0) | 1 ( 5.0) | ||
| 40 | 0 ( 0.0) | 2 ( 10.0) | ||
| 50 | 0 ( 0.0) | 1 ( 5.0) | ||
| TII_pr_viemar = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| TII_kuiv_mat12345 (%) | 0.508 | |||
| 1 | 4 ( 17.4) | 1 ( 5.0) | ||
| 2 | 15 ( 65.2) | 16 ( 80.0) | ||
| 12 | 1 ( 4.3) | 2 ( 10.0) | ||
| 14 | 2 ( 8.7) | 1 ( 5.0) | ||
| 15 | 1 ( 4.3) | 0 ( 0.0) | ||
| TII_kuiv_5_mika = 2 (%) | 1 ( 4.3) | 0 ( 0.0) | 1.000 | |
| TII_maara1234 (%) | 0.669 | |||
| 1 | 3 ( 13.0) | 1 ( 5.0) | ||
| 2 | 2 ( 8.7) | 3 ( 15.0) | ||
| 3 | 14 ( 60.9) | 11 ( 55.0) | ||
| 4 | 4 ( 17.4) | 4 ( 20.0) | ||
| 23 | 0 ( 0.0) | 1 ( 5.0) | ||
| TII_tonkimat_6_mika (%) | 0.440 | |||
| 1 | 22 ( 95.7) | 16 ( 80.0) | ||
| 2 | 0 ( 0.0) | 1 ( 5.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.0) | ||
| 4 | 0 ( 0.0) | 1 ( 5.0) | ||
| 5 | 1 ( 4.3) | 1 ( 5.0) | ||
| TII_lelu1234 (%) | 0.257 | |||
| 2 | 2 ( 8.7) | 0 ( 0.0) | ||
| 4 | 21 ( 91.3) | 18 ( 90.0) | ||
| 5 | 0 ( 0.0) | 1 ( 5.0) | ||
| 24 | 0 ( 0.0) | 1 ( 5.0) | ||
| TII_mat_vaiht = 1 (%) | 23 (100.0) | 19 ( 95.0) | 0.944 | |
| TII_maara123 (%) | 0.844 | |||
| 1 | 3 ( 13.0) | 1 ( 5.0) | ||
| 1.5 | 1 ( 4.3) | 1 ( 5.0) | ||
| 2 | 18 ( 78.3) | 17 ( 85.0) | ||
| 3 | 1 ( 4.3) | 1 ( 5.0) | ||
| TII_annostelu1234 (%) | 0.550 | |||
| 1 | 22 ( 95.7) | 18 ( 90.0) | ||
| 2 | 0 ( 0.0) | 1 ( 5.0) | ||
| 4 | 1 ( 4.3) | 1 ( 5.0) | ||
| TII_lannanpoisto12 (%) | 0.168 | |||
| 1 | 15 ( 65.2) | 8 ( 40.0) | ||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| 3 | 1 ( 4.3) | 4 ( 20.0) | ||
| 4 | 1 ( 4.3) | 0 ( 0.0) | ||
| 5 | 5 ( 21.7) | 8 ( 40.0) | ||
| TII_rak_kunto = 1 (%) | 0 ( 0.0) | 2 ( 10.0) | 0.408 | |
| TII_latt_pitava = 1 (%) | 1 ( 4.3) | 2 ( 10.0) | 0.900 | |
| TII_sairkars = 1 (%) | 21 ( 91.3) | 18 ( 90.0) | 1.000 | |
| TII_ruok_0nonlock_1lock = 1 (%) | 11 ( 47.8) | 6 ( 30.0) | 0.379 | |
| TII_ruokpuht = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| TII_juomalaite123 (%) | 0.364 | |||
| 1 | 22 ( 95.7) | 19 ( 95.0) | ||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| 12 | 0 ( 0.0) | 1 ( 5.0) | ||
| TII_juomapuht = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| TII_juomatoim = 2 (%) | 1 ( 4.3) | 0 ( 0.0) | 1.000 | |
| TII_rauhallisuus123 = 2 (%) | 0 ( 0.0) | 1 ( 5.0) | 0.944 | |
| TII_hoitotarveKE = 2 (%) | 11 ( 47.8) | 11 ( 55.0) | 0.870 | |
| TII_stereo = 1 (%) | 2 ( 8.7) | 2 ( 10.0) | 1.000 | |
| POR_meluton = 1 (%) | 18 ( 78.3) | 15 ( 75.0) | 1.000 | |
| POR_haittael_ei = 1 (%) | 19 ( 82.6) | 18 ( 90.0) | 0.798 | |
| POR_haittael_laatu (%) | 0.383 | |||
| 1 | 15 ( 65.2) | 10 ( 50.0) | ||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.0) | ||
| 4 | 7 ( 30.4) | 9 ( 45.0) | ||
| POR_ilma_aistin = 1 (%) | 1 ( 4.3) | 0 ( 0.0) | 1.000 | |
| POR_ilma_amm = 1 (%) | 1 ( 4.3) | 0 ( 0.0) | 1.000 | |
| POR_ilma_pöly = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| POR_ilma_muu = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| POR_kosteus = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| POR_valaistus (%) | 0.234 | |||
| 0 | 21 ( 91.3) | 19 ( 95.0) | ||
| 0.5 | 0 ( 0.0) | 1 ( 5.0) | ||
| 1 | 2 ( 8.7) | 0 ( 0.0) | ||
| POR_latt_rakenne1234 (%) | 0.387 | |||
| 1 | 2 ( 8.7) | 0 ( 0.0) | ||
| 2 | 2 ( 8.7) | 1 ( 5.0) | ||
| 12 | 18 ( 78.3) | 18 ( 90.0) | ||
| 13 | 0 ( 0.0) | 1 ( 5.0) | ||
| 123 | 1 ( 4.3) | 0 ( 0.0) | ||
| POR_pr_rako = 38 (%) | 0 ( 0.0) | 1 ( 5.0) | 0.944 | |
| POR_maara1234 (%) | 0.020 | |||
| 2 | 1 ( 4.3) | 7 ( 35.0) | ||
| 3 | 17 ( 73.9) | 12 ( 60.0) | ||
| 4 | 5 ( 21.7) | 1 ( 5.0) | ||
| POR_tonkimat_6_mika (%) | 0.307 | |||
| 1 | 21 ( 91.3) | 15 ( 75.0) | ||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.0) | ||
| 4 | 1 ( 4.3) | 3 ( 15.0) | ||
| 5 | 0 ( 0.0) | 1 ( 5.0) | ||
| POR_lelu1234 (%) | 0.216 | |||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 2 ( 10.0) | ||
| 4 | 22 ( 95.7) | 17 ( 85.0) | ||
| 5 | 0 ( 0.0) | 1 ( 5.0) | ||
| POR_lelukomm (%) | 0.423 | |||
| 1 | 20 ( 87.0) | 20 (100.0) | ||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| 3 | 1 ( 4.3) | 0 ( 0.0) | ||
| 4 | 1 ( 4.3) | 0 ( 0.0) | ||
| POR_mat_vaiht = 2 (%) | 1 ( 4.3) | 0 ( 0.0) | 1.000 | |
| POR_maara123 (%) | 0.401 | |||
| 1 | 2 ( 8.7) | 0 ( 0.0) | ||
| 2 | 20 ( 87.0) | 19 ( 95.0) | ||
| 3 | 1 ( 4.3) | 1 ( 5.0) | ||
| POR_annostelu1234 (%) | 0.763 | |||
| 1 | 19 ( 82.6) | 18 ( 90.0) | ||
| 2 | 2 ( 8.7) | 1 ( 5.0) | ||
| 3 | 1 ( 4.3) | 1 ( 5.0) | ||
| 4 | 1 ( 4.3) | 0 ( 0.0) | ||
| POR_lannanpoisto12 = 2 (%) | 20 ( 87.0) | 19 ( 95.0) | 0.704 | |
| POR_rak_kunto = 1 (%) | 1 ( 4.3) | 2 ( 10.0) | 0.900 | |
| POR_latt_pitava = 1 (%) | 3 ( 13.0) | 0 ( 0.0) | 0.283 | |
| POR_sairkars (%) | 0.674 | |||
| 1 | 14 ( 60.9) | 15 ( 75.0) | ||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| 3 | 2 ( 8.7) | 2 ( 10.0) | ||
| 4 | 5 ( 21.7) | 3 ( 15.0) | ||
| 5 | 1 ( 4.3) | 0 ( 0.0) | ||
| POR_ruoklaite12345 (%) | 0.257 | |||
| 2 | 0 ( 0.0) | 2 ( 10.0) | ||
| 2.5 | 21 ( 91.3) | 18 ( 90.0) | ||
| 3 | 1 ( 4.3) | 0 ( 0.0) | ||
| 25 | 1 ( 4.3) | 0 ( 0.0) | ||
| POR_ruokpaikka = 1 (%) | 23 (100.0) | 20 (100.0) | NA | |
| POR_ruokpuht = 1 (%) | 0 ( 0.0) | 3 ( 15.0) | 0.185 | |
| POR_juomalaite123 = 13 (%) | 0 ( 0.0) | 1 ( 5.0) | 0.944 | |
| POR_juonalkm = 1 (%) | 23 (100.0) | 20 (100.0) | NA | |
| POR_juomapuht = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| POR_juomatoim = 1 (%) | 0 ( 0.0) | 1 ( 5.0) | 0.944 | |
| POR_rauhallisuus123 = 1 (%) | 23 (100.0) | 20 (100.0) | NA | |
| Hajukarjut_per_emakko (%) | 0.560 | |||
| 0 | 3 ( 13.0) | 4 ( 20.0) | ||
| 0.01 | 13 ( 56.5) | 12 ( 60.0) | ||
| 0.02 | 3 ( 13.0) | 2 ( 10.0) | ||
| 0.03 | 4 ( 17.4) | 1 ( 5.0) | ||
| 0.06 | 0 ( 0.0) | 1 ( 5.0) | ||
| TII_VIRMaa_0_ei_1pellel_2pelvir_3niukuihiemnvir_4riirunkuiv (%) | 0.339 | |||
| 0 | 1 ( 4.3) | 3 ( 15.0) | ||
| 1 | 0 ( 0.0) | 2 ( 10.0) | ||
| 2 | 4 ( 17.4) | 4 ( 20.0) | ||
| 3 | 6 ( 26.1) | 4 ( 20.0) | ||
| 4 | 12 ( 52.2) | 7 ( 35.0) | ||
| TII_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme (%) | 0.246 | |||
| 0 | 1 ( 4.3) | 3 ( 15.0) | ||
| 1 | 3 ( 13.0) | 6 ( 30.0) | ||
| 2 | 12 ( 52.2) | 6 ( 30.0) | ||
| 3 | 7 ( 30.4) | 5 ( 25.0) | ||
| AS_VIRMaa_0ei_1pellel_2pelvir_3niukuihiemvir_4riirunkuiv (%) | 0.019 | |||
| 0 | 4 ( 17.4) | 1 ( 5.0) | ||
| 1 | 0 ( 0.0) | 7 ( 35.0) | ||
| 2 | 12 ( 52.2) | 5 ( 25.0) | ||
| 3 | 4 ( 17.4) | 5 ( 25.0) | ||
| 4 | 3 ( 13.0) | 2 ( 10.0) | ||
| AS_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme (%) | 0.227 | |||
| 0 | 4 ( 17.4) | 1 ( 5.0) | ||
| 1 | 9 ( 39.1) | 13 ( 65.0) | ||
| 2 | 6 ( 26.1) | 2 ( 10.0) | ||
| 3 | 4 ( 17.4) | 4 ( 20.0) | ||
| POR_VIRMaa_0_ei_1pellel_2pelvir_3niukui_4riikuiv (%) | 0.194 | |||
| 0 | 2 ( 8.7) | 1 ( 5.0) | ||
| 1 | 1 ( 4.3) | 2 ( 10.0) | ||
| 2 | 7 ( 30.4) | 2 ( 10.0) | ||
| 3 | 10 ( 43.5) | 7 ( 35.0) | ||
| 4 | 3 ( 13.0) | 8 ( 40.0) | ||
| POR_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme (%) | 0.851 | |||
| 0 | 1 ( 4.3) | 1 ( 5.0) | ||
| 1 | 6 ( 26.1) | 3 ( 15.0) | ||
| 2 | 14 ( 60.9) | 14 ( 70.0) | ||
| 3 | 2 ( 8.7) | 2 ( 10.0) | ||
| Koulmax_1peru_2ops_3a_4amk_5yl (%) | 0.613 | |||
| 2 | 3 ( 13.0) | 2 ( 10.0) | ||
| 3 | 15 ( 65.2) | 13 ( 65.0) | ||
| 4 | 4 ( 17.4) | 2 ( 10.0) | ||
| 5 | 1 ( 4.3) | 3 ( 15.0) | ||
| Stressi_1erpal_4jnkv (%) | 0.846 | |||
| 1 | 4 ( 17.4) | 2 ( 10.0) | ||
| 2 | 4 ( 17.4) | 4 ( 20.0) | ||
| 3 | 8 ( 34.8) | 6 ( 30.0) | ||
| 4 | 7 ( 30.4) | 8 ( 40.0) | ||
| EMKUOLLJAKO = 1 (%) | 0 ( 0.0) | 20 (100.0) | <0.001 | |
| EMPOISJAKO = 1 (%) | 5 ( 21.7) | 14 ( 70.0) | 0.004 | |
| EMENKUOLLJAKO = 1 (%) | 1 ( 4.3) | 17 ( 85.0) | <0.001 | |
| EMENPOISJAKO = 1 (%) | 3 ( 13.0) | 13 ( 65.0) | 0.001 | |
| NIVEL_01 = 2 (%) | 8 ( 34.8) | 10 ( 50.0) | 0.485 | |
| PAISE_01 = 2 (%) | 8 ( 34.8) | 10 ( 50.0) | 0.485 | |
| MAKUU01 = 2 (%) | 7 ( 30.4) | 11 ( 55.0) | 0.187 | |
| KOKO_01 = 2 (%) | 7 ( 30.4) | 11 ( 55.0) | 0.187 | |
| OSA_01 = 2 (%) | 9 ( 39.1) | 9 ( 45.0) | 0.937 | |
| JOKUHYLK_01 = 2 (%) | 6 ( 26.1) | 12 ( 60.0) | 0.053 | |
| PLEUR_01 = 1 (%) | 4 ( 17.4) | 8 ( 40.0) | 0.191 | |
| PNEUM_01 = 2 (%) | 7 ( 30.4) | 11 ( 55.0) | 0.187 | |
| SAIRKARS_AST_TII = 1 (%) | 15 ( 65.2) | 18 ( 90.0) | 0.120 | |
| EMKUOL (mean (sd)) | 0.00 (0.00) | 1.00 (0.00) | <0.001 |
Yhteenveto poistojen mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
tilatkat2<-tilatkat
tilatkat2$EMPOIS<-tilat$EMPOISJAKO
table2 = KreateTableOne(x=tilatkat2, strata='EMPOIS')table2%>%
kable("html", align = "rrr", caption = "Data variable summary strat by EMPOIS") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 24 | 19 | ||
| Haastrooli_1OmEiosall_2OmOsall_3Esimies (%) | 0.311 | |||
| 1 | 4 ( 16.7) | 6 ( 31.6) | ||
| 2 | 18 ( 75.0) | 10 ( 52.6) | ||
| 3 | 2 ( 8.3) | 3 ( 15.8) | ||
| Tuotsuunta = 2 (%) | 13 ( 54.2) | 8 ( 42.1) | 0.632 | |
| Karjut_astsiem (%) | 0.245 | |||
| 0 | 22 ( 91.7) | 16 ( 84.2) | ||
| 1 | 1 ( 4.2) | 0 ( 0.0) | ||
| 2 | 1 ( 4.2) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.3) | ||
| 6 | 0 ( 0.0) | 2 ( 10.5) | ||
| Tautsu = 1 (%) | 15 ( 62.5) | 15 ( 78.9) | 0.405 | |
| Tautsuok = 1 (%) | 9 ( 37.5) | 12 ( 63.2) | 0.172 | |
| Tautsu_012 (%) | 0.473 | |||
| 0 | 9 ( 37.5) | 4 ( 21.1) | ||
| 1 | 7 ( 29.2) | 6 ( 31.6) | ||
| 2 | 8 ( 33.3) | 9 ( 47.4) | ||
| Siilotkat = 1 (%) | 22 ( 91.7) | 17 ( 89.5) | 1.000 | |
| Tuhoei = 1 (%) | 20 ( 83.3) | 15 ( 78.9) | 1.000 | |
| Eikulkuih = 1 (%) | 16 ( 66.7) | 11 ( 57.9) | 0.785 | |
| Eikulkuel = 1 (%) | 17 ( 70.8) | 13 ( 68.4) | 1.000 | |
| Suojvar = 1 (%) | 24 (100.0) | 19 (100.0) | NA | |
| Suojvarpuh = 1 (%) | 23 ( 95.8) | 19 (100.0) | 1.000 | |
| Kadetpesu = 1 (%) | 16 ( 66.7) | 14 ( 73.7) | 0.870 | |
| Toimsiis = 1 (%) | 23 ( 95.8) | 18 ( 94.7) | 1.000 | |
| Saappesu = 1 (%) | 18 ( 75.0) | 13 ( 68.4) | 0.892 | |
| Lasthu = 1 (%) | 19 ( 79.2) | 17 ( 89.5) | 0.622 | |
| Teurkuski_0paaseesikalaan_1eipaase = 1 (%) | 18 ( 75.0) | 12 ( 63.2) | 0.613 | |
| JOU_kertayt_0ei = 1 (%) | 4 ( 16.7) | 3 ( 15.8) | 1.000 | |
| JOU_tuotvaiherill_0ei = 1 (%) | 18 ( 75.0) | 13 ( 68.4) | 0.892 | |
| JOU_pesu_0ei = 1 (%) | 4 ( 16.7) | 2 ( 10.5) | 0.893 | |
| JOU_pesuaine_0ei = 1 (%) | 3 ( 12.5) | 1 ( 5.3) | 0.777 | |
| JOU_desinf_liu_0ei_1liuos_2kuiva (%) | 0.709 | |||
| 0 | 22 ( 91.7) | 16 ( 84.2) | ||
| 1 | 0 ( 0.0) | 1 ( 5.3) | ||
| 2 | 1 ( 4.2) | 1 ( 5.3) | ||
| 12 | 1 ( 4.2) | 1 ( 5.3) | ||
| JOU_tyhjana_mi1vrk_0ei = 1 (%) | 9 ( 37.5) | 4 ( 21.1) | 0.405 | |
| PORSOSASTO_kertayt_0ei = 1 (%) | 8 ( 33.3) | 10 ( 52.6) | 0.336 | |
| PORS_tuotvaiherill_0ei = 1 (%) | 17 ( 70.8) | 16 ( 84.2) | 0.504 | |
| PORS_pesu_0ei = 1 (%) | 20 ( 83.3) | 13 ( 68.4) | 0.432 | |
| PORS_pesuaine_0ei = 1 (%) | 5 ( 20.8) | 5 ( 26.3) | 0.953 | |
| PORS_desinf_0ei_1LIU_2KUIVA (%) | 0.894 | |||
| 0 | 4 ( 16.7) | 5 ( 26.3) | ||
| 1 | 11 ( 45.8) | 8 ( 42.1) | ||
| 2 | 6 ( 25.0) | 4 ( 21.1) | ||
| 12 | 3 ( 12.5) | 2 ( 10.5) | ||
| PORS_tyhjana_mi1vr_0ei = 1 (%) | 14 ( 58.3) | 12 ( 63.2) | 0.994 | |
| Raa_0ei_1kontti_2huone (%) | 0.844 | |||
| 0 | 1 ( 4.2) | 2 ( 10.5) | ||
| 1 | 20 ( 83.3) | 14 ( 73.7) | ||
| 2 | 1 ( 4.2) | 1 ( 5.3) | ||
| 12 | 2 ( 8.3) | 2 ( 10.5) | ||
| Raa_auto_hakee_0ei = 1 (%) | 17 ( 70.8) | 10 ( 52.6) | 0.364 | |
| Raa_viilea_0ei = 1 (%) | 22 ( 91.7) | 16 ( 84.2) | 0.781 | |
| Raa_tuhoelain_1eipaase_0paaseesic = 1 (%) | 15 ( 62.5) | 11 ( 57.9) | 1.000 | |
| Tuhoelmerkkeja_0kylla_1ei = 1 (%) | 6 ( 25.0) | 4 ( 21.1) | 1.000 | |
| Lintuja_0kylla_1ei = 1 (%) | 19 ( 79.2) | 14 ( 73.7) | 0.953 | |
| Tuho_ohjelma = 1 (%) | 2 ( 8.3) | 3 ( 15.8) | 0.781 | |
| kissoja0on1ei (%) | 0.051 | |||
| 0 | 18 ( 75.0) | 8 ( 42.1) | ||
| 0.5 | 0 ( 0.0) | 2 ( 10.5) | ||
| 1 | 6 ( 25.0) | 9 ( 47.4) | ||
| Kotielain_sikalaan_0kylla_1ei = 1 (%) | 20 ( 83.3) | 14 ( 73.7) | 0.693 | |
| Vesi_1kunn_0oma = 1 (%) | 15 ( 62.5) | 12 ( 63.2) | 1.000 | |
| Ery = 1 (%) | 24 (100.0) | 19 (100.0) | NA | |
| Parvo = 1 (%) | 24 (100.0) | 19 (100.0) | NA | |
| Koli = 1 (%) | 23 ( 95.8) | 18 ( 94.7) | 1.000 | |
| Sirko = 1 (%) | 8 ( 33.3) | 5 ( 26.3) | 0.870 | |
| ClC = 1 (%) | 1 ( 4.2) | 2 ( 10.5) | 0.833 | |
| ClA = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| SI = 1 (%) | 1 ( 4.2) | 3 ( 15.8) | 0.439 | |
| APP = 1 (%) | 4 ( 16.7) | 1 ( 5.3) | 0.497 | |
| Loisaika_1ennenpors_2_porskars = 2 (%) | 8 ( 33.3) | 8 ( 42.1) | 0.785 | |
| Uusiryh (%) | 0.597 | |||
| 1 | 2 ( 8.3) | 1 ( 5.3) | ||
| 2 | 20 ( 83.3) | 18 ( 94.7) | ||
| 3 | 1 ( 4.2) | 0 ( 0.0) | ||
| 4 | 1 ( 4.2) | 0 ( 0.0) | ||
| Ton_tiheys_1aina_2jaetaan = 2 (%) | 3 ( 12.5) | 1 ( 5.3) | 0.777 | |
| Yhdistaggrtmp_1eiongelma_2tmp_3eitmp (%) | 0.591 | |||
| 1 | 4 ( 16.7) | 1 ( 5.3) | ||
| 2 | 11 ( 45.8) | 11 ( 57.9) | ||
| 3 | 4 ( 16.7) | 2 ( 10.5) | ||
| 12 | 5 ( 20.8) | 5 ( 26.3) | ||
| Muutelkaynn_0ei_1_satunn_2kaynnmuusaann (%) | 0.089 | |||
| 0 | 11 ( 45.8) | 5 ( 26.3) | ||
| 1 | 12 ( 50.0) | 9 ( 47.4) | ||
| 2 | 1 ( 4.2) | 5 ( 26.3) | ||
| maitokuume = 1 (%) | 12 ( 50.0) | 10 ( 52.6) | 1.000 | |
| metriitti = 1 (%) | 10 ( 41.7) | 9 ( 47.4) | 0.948 | |
| valuttelu = 1 (%) | 3 ( 12.5) | 2 ( 10.5) | 1.000 | |
| mastiitti = 1 (%) | 5 ( 20.8) | 5 ( 26.3) | 0.953 | |
| ontuma = 1 (%) | 15 ( 62.5) | 16 ( 84.2) | 0.217 | |
| syomattomyys = 1 (%) | 14 ( 58.3) | 8 ( 42.1) | 0.453 | |
| kuume = 1 (%) | 5 ( 20.8) | 1 ( 5.3) | 0.308 | |
| loukkaantuminen = 1 (%) | 11 ( 45.8) | 5 ( 26.3) | 0.319 | |
| AB_rutiinilaak = 1 (%) | 3 ( 12.5) | 3 ( 15.8) | 1.000 | |
| Oksitosiini_rutiinisti = 1 (%) | 7 ( 29.2) | 10 ( 52.6) | 0.212 | |
| Kaynnistys_rutiinisti = 1 (%) | 0 ( 0.0) | 4 ( 21.1) | 0.067 | |
| NSAID_porsituksessa_rutiini = 1 (%) | 6 ( 25.0) | 4 ( 21.1) | 1.000 | |
| OMATENSIKOT_0EI_1KYLLa = 1 (%) | 15 ( 62.5) | 13 ( 68.4) | 0.934 | |
| Ensikk_valisiirtkars_ennensiem = 1 (%) | 8 ( 33.3) | 9 ( 47.4) | 0.535 | |
| Ensikk_kiihruok = 1 (%) | 9 ( 37.5) | 7 ( 36.8) | 1.000 | |
| Ensikk_karjukontaktiensi_0hajutainako_1aidanlapi_2kars (%) | 0.431 | |||
| 0 | 2 ( 8.3) | 2 ( 10.5) | ||
| 1 | 20 ( 83.3) | 17 ( 89.5) | ||
| 2 | 2 ( 8.3) | 0 ( 0.0) | ||
| siemika (%) | 0.161 | |||
| 7 | 1 ( 4.2) | 0 ( 0.0) | ||
| 7.5 | 0 ( 0.0) | 2 ( 10.5) | ||
| 8 | 20 ( 83.3) | 14 ( 73.7) | ||
| 8.5 | 1 ( 4.2) | 3 ( 15.8) | ||
| 9.5 | 2 ( 8.3) | 0 ( 0.0) | ||
| Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk (%) | 0.828 | |||
| 1 | 1 ( 4.2) | 0 ( 0.0) | ||
| 2 | 3 ( 12.5) | 2 ( 10.5) | ||
| 3 | 19 ( 79.2) | 16 ( 84.2) | ||
| 4 | 1 ( 4.2) | 1 ( 5.3) | ||
| Kiimantark_ryhmakaytt = 1 (%) | 21 ( 87.5) | 17 ( 89.5) | 1.000 | |
| Kiimantarkalkaa_vrkvier (%) | 0.224 | |||
| 0 | 5 ( 20.8) | 7 ( 36.8) | ||
| 1 | 16 ( 66.7) | 7 ( 36.8) | ||
| 3 | 2 ( 8.3) | 1 ( 5.3) | ||
| 4 | 0 ( 0.0) | 1 ( 5.3) | ||
| 5 | 1 ( 4.2) | 3 ( 15.8) | ||
| Kiimamerk_emakonselka = 1 (%) | 18 ( 75.0) | 19 (100.0) | 0.057 | |
| Kiimantark_postsiem = 1 (%) | 23 ( 95.8) | 18 ( 94.7) | 1.000 | |
| Postsiem_ryhmakaytt_havainnointi = 1 (%) | 21 ( 87.5) | 17 ( 89.5) | 1.000 | |
| Tiin_ultra2 (%) | 0.266 | |||
| 6 | 24 (100.0) | 17 ( 89.5) | ||
| 8 | 0 ( 0.0) | 1 ( 5.3) | ||
| 10 | 0 ( 0.0) | 1 ( 5.3) | ||
| Tiin_ultra_1yhdesti_2kahdesti (%) | 0.098 | |||
| 0 | 6 ( 25.0) | 1 ( 5.3) | ||
| 1 | 16 ( 66.7) | 13 ( 68.4) | ||
| 2 | 2 ( 8.3) | 5 ( 26.3) | ||
| Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen (%) | 0.085 | |||
| 0 | 10 ( 41.7) | 6 ( 31.6) | ||
| 1 | 0 ( 0.0) | 2 ( 10.5) | ||
| 2 | 2 ( 8.3) | 6 ( 31.6) | ||
| 3 | 2 ( 8.3) | 0 ( 0.0) | ||
| 4 | 10 ( 41.7) | 5 ( 26.3) | ||
| Pesantekomatmaara_1runsas_2jnkv_3niukka (%) | 0.269 | |||
| 1 | 3 ( 12.5) | 0 ( 0.0) | ||
| 2 | 17 ( 70.8) | 16 ( 84.2) | ||
| 3 | 4 ( 16.7) | 3 ( 15.8) | ||
| Sisatutk_ennenoksitos = 1 (%) | 10 ( 41.7) | 5 ( 26.3) | 0.467 | |
| Porsitusaputekn_1empesu_2kaspesu_3kasine_4liukaste (%) | 0.532 | |||
| 34 | 10 ( 41.7) | 5 ( 26.3) | ||
| 124 | 1 ( 4.2) | 1 ( 5.3) | ||
| 134 | 7 ( 29.2) | 10 ( 52.6) | ||
| 234 | 1 ( 4.2) | 0 ( 0.0) | ||
| 1234 | 5 ( 20.8) | 3 ( 15.8) | ||
| PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa = 2 (%) | 20 ( 83.3) | 15 ( 78.9) | 1.000 | |
| Ruoksu_0ei_1itse_2neuvoja_3kyllaeitietoa (%) | 0.445 | |||
| 1 | 1 ( 4.2) | 1 ( 5.3) | ||
| 2 | 21 ( 87.5) | 13 ( 68.4) | ||
| 3 | 1 ( 4.2) | 2 ( 10.5) | ||
| 12 | 1 ( 4.2) | 3 ( 15.8) | ||
| Yksilöll_ruokinta = 1 (%) | 16 ( 66.7) | 15 ( 78.9) | 0.583 | |
| AS_1ast_jout_samassa_2asteiole = 2 (%) | 21 ( 87.5) | 15 ( 78.9) | 0.735 | |
| AS_er_os_lkm = 2 (%) | 3 ( 12.5) | 1 ( 5.3) | 0.777 | |
| AS_em_kars (%) | 0.391 | |||
| 2.5 | 1 ( 4.2) | 0 ( 0.0) | ||
| 7 | 0 ( 0.0) | 1 ( 5.3) | ||
| 7.5 | 22 ( 91.7) | 17 ( 89.5) | ||
| 8 | 0 ( 0.0) | 1 ( 5.3) | ||
| 60 | 1 ( 4.2) | 0 ( 0.0) | ||
| AS_karspit (%) | 0.448 | |||
| 3.31 | 1 ( 4.2) | 0 ( 0.0) | ||
| 4.4 | 1 ( 4.2) | 0 ( 0.0) | ||
| 5.94 | 21 ( 87.5) | 18 ( 94.7) | ||
| 7 | 0 ( 0.0) | 1 ( 5.3) | ||
| 20 | 1 ( 4.2) | 0 ( 0.0) | ||
| AS_karslev (%) | 0.448 | |||
| 2.67 | 1 ( 4.2) | 0 ( 0.0) | ||
| 3.02 | 0 ( 0.0) | 1 ( 5.3) | ||
| 4.8 | 21 ( 87.5) | 18 ( 94.7) | ||
| 6.8 | 1 ( 4.2) | 0 ( 0.0) | ||
| 7 | 1 ( 4.2) | 0 ( 0.0) | ||
| AS_meluton = 1 (%) | 21 ( 87.5) | 18 ( 94.7) | 0.777 | |
| AS_haittael_ei = 1 (%) | 21 ( 87.5) | 16 ( 84.2) | 1.000 | |
| AS_haittael_laatu (%) | 0.246 | |||
| 1 | 16 ( 66.7) | 10 ( 52.6) | ||
| 2 | 0 ( 0.0) | 2 ( 10.5) | ||
| 3 | 0 ( 0.0) | 1 ( 5.3) | ||
| 4 | 8 ( 33.3) | 6 ( 31.6) | ||
| AS_ilma_aistin = 1 (%) | 4 ( 16.7) | 4 ( 21.1) | 1.000 | |
| AS_ilma_amm = 1 (%) | 4 ( 16.7) | 4 ( 21.1) | 1.000 | |
| AS_ilma_pöly = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| AS_ilma_muu = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| AS_kosteus = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| AS_valaistus = 1 (%) | 1 ( 4.2) | 1 ( 5.3) | 1.000 | |
| AS_alusta12345 = 12 (%) | 3 ( 12.5) | 4 ( 21.1) | 0.735 | |
| AS_alusta_5_laatu = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| AS_latt_rakenne1234 = 13 (%) | 21 ( 87.5) | 15 ( 78.9) | 0.735 | |
| AS_pr_ritila (%) | 0.594 | |||
| 0 | 21 ( 87.5) | 15 ( 78.9) | ||
| 20 | 1 ( 4.2) | 2 ( 10.5) | ||
| 25 | 1 ( 4.2) | 1 ( 5.3) | ||
| 33 | 0 ( 0.0) | 1 ( 5.3) | ||
| 41 | 1 ( 4.2) | 0 ( 0.0) | ||
| AS_pr_viemar = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| AS_kuiv_mat12345 (%) | 0.315 | |||
| 1 | 3 ( 12.5) | 1 ( 5.3) | ||
| 1.5 | 19 ( 79.2) | 16 ( 84.2) | ||
| 2 | 0 ( 0.0) | 2 ( 10.5) | ||
| 12 | 1 ( 4.2) | 0 ( 0.0) | ||
| 14 | 1 ( 4.2) | 0 ( 0.0) | ||
| AS_kuiv_5_mika (%) | 0.513 | |||
| 0 | 1 ( 4.2) | 1 ( 5.3) | ||
| 3 | 23 ( 95.8) | 17 ( 89.5) | ||
| 4 | 0 ( 0.0) | 1 ( 5.3) | ||
| AS_maara1234 (%) | 0.633 | |||
| 0 | 1 ( 4.2) | 0 ( 0.0) | ||
| 1 | 1 ( 4.2) | 0 ( 0.0) | ||
| 2 | 1 ( 4.2) | 0 ( 0.0) | ||
| 3 | 2 ( 8.3) | 2 ( 10.5) | ||
| 4 | 19 ( 79.2) | 17 ( 89.5) | ||
| AS_tonkimat123456 (%) | 0.358 | |||
| 1 | 23 ( 95.8) | 18 ( 94.7) | ||
| 5 | 0 ( 0.0) | 1 ( 5.3) | ||
| 12 | 1 ( 4.2) | 0 ( 0.0) | ||
| AS_tonkimat_6_mika = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| AS_mat_vaiht = 1 (%) | 22 ( 91.7) | 19 (100.0) | 0.576 | |
| AS_maara123 (%) | 0.320 | |||
| 0 | 1 ( 4.2) | 0 ( 0.0) | ||
| 2 | 19 ( 79.2) | 18 ( 94.7) | ||
| 3 | 4 ( 16.7) | 1 ( 5.3) | ||
| AS_annostelu1234 (%) | 0.660 | |||
| 0 | 1 ( 4.2) | 1 ( 5.3) | ||
| 1 | 22 ( 91.7) | 18 ( 94.7) | ||
| 3 | 1 ( 4.2) | 0 ( 0.0) | ||
| AS_lannanpoisto12 (%) | 0.531 | |||
| 0 | 1 ( 4.2) | 0 ( 0.0) | ||
| 1 | 1 ( 4.2) | 2 ( 10.5) | ||
| 2 | 21 ( 87.5) | 17 ( 89.5) | ||
| 12 | 1 ( 4.2) | 0 ( 0.0) | ||
| AS_rak_kunto = 1 (%) | 1 ( 4.2) | 0 ( 0.0) | 1.000 | |
| AS_latt_pitava = 1 (%) | 1 ( 4.2) | 0 ( 0.0) | 1.000 | |
| AS_sairkars = 1 (%) | 6 ( 25.0) | 5 ( 26.3) | 1.000 | |
| AS_sk_parempi (%) | 0.461 | |||
| 0 | 4 ( 16.7) | 2 ( 10.5) | ||
| 0.5 | 0 ( 0.0) | 1 ( 5.3) | ||
| 1 | 20 ( 83.3) | 16 ( 84.2) | ||
| AS_sk_kiintea = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| AS_sk_kuivike (%) | 0.916 | |||
| 0 | 21 ( 87.5) | 17 ( 89.5) | ||
| 0.5 | 1 ( 4.2) | 1 ( 5.3) | ||
| 1 | 2 ( 8.3) | 1 ( 5.3) | ||
| AS_sk_siisti = 1 (%) | 2 ( 8.3) | 1 ( 5.3) | 1.000 | |
| AS_sk_kuiva (%) | 0.358 | |||
| 0 | 23 ( 95.8) | 18 ( 94.7) | ||
| 0.5 | 0 ( 0.0) | 1 ( 5.3) | ||
| 1 | 1 ( 4.2) | 0 ( 0.0) | ||
| AS_sk_syörauha = 1 (%) | 1 ( 4.2) | 4 ( 21.1) | 0.216 | |
| AS_sk_juorauha = 1 (%) | 1 ( 4.2) | 4 ( 21.1) | 0.216 | |
| AS_ruoklaite12345 = 4 (%) | 24 (100.0) | 17 ( 89.5) | 0.369 | |
| AS_ruokpaikka (%) | 0.266 | |||
| 0 | 0 ( 0.0) | 1 ( 5.3) | ||
| 1 | 24 (100.0) | 17 ( 89.5) | ||
| 4 | 0 ( 0.0) | 1 ( 5.3) | ||
| AS_ruokpuht = 1 (%) | 3 ( 12.5) | 3 ( 15.8) | 1.000 | |
| AS_juomalaite123 = 1 (%) | 23 ( 95.8) | 19 (100.0) | 1.000 | |
| AS_juonalkm (%) | 0.358 | |||
| 0.222222222222222 | 0 ( 0.0) | 1 ( 5.3) | ||
| 1 | 23 ( 95.8) | 18 ( 94.7) | ||
| 2.25 | 1 ( 4.2) | 0 ( 0.0) | ||
| AS_juomapuht = 1 (%) | 1 ( 4.2) | 0 ( 0.0) | 1.000 | |
| AS_juomatoim = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| AS_rauhallisuus123 = 1 (%) | 22 ( 91.7) | 19 (100.0) | 0.576 | |
| AS_hoitotarveKE = 2 (%) | 10 ( 41.7) | 9 ( 47.4) | 0.948 | |
| AS_stereo = 1 (%) | 4 ( 16.7) | 2 ( 10.5) | 0.893 | |
| TII_1ast_jout_samassa_2asteiole (%) | 0.953 | |||
| 0 | 21 ( 87.5) | 16 ( 84.2) | ||
| 1 | 2 ( 8.3) | 2 ( 10.5) | ||
| 2 | 1 ( 4.2) | 1 ( 5.3) | ||
| TII_valiseinat (%) | 0.491 | |||
| 0 | 22 ( 91.7) | 16 ( 84.2) | ||
| 0.5 | 0 ( 0.0) | 1 ( 5.3) | ||
| 1 | 1 ( 4.2) | 1 ( 5.3) | ||
| 2.5 | 1 ( 4.2) | 0 ( 0.0) | ||
| 16 | 0 ( 0.0) | 1 ( 5.3) | ||
| TII_meluton = 1 (%) | 17 ( 70.8) | 17 ( 89.5) | 0.265 | |
| TII_haittael_ei = 1 (%) | 19 ( 79.2) | 14 ( 73.7) | 0.953 | |
| TII_ilma_aistin = 1 (%) | 2 ( 8.3) | 3 ( 15.8) | 0.781 | |
| TII_ilma_amm = 1 (%) | 3 ( 12.5) | 3 ( 15.8) | 1.000 | |
| TII_ilma_pöly = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| TII_ilma_muu = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| TII_kosteus = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| TII_valaistus = 1 (%) | 1 ( 4.2) | 0 ( 0.0) | 1.000 | |
| TII_alusta12345 = 1 (%) | 24 (100.0) | 19 (100.0) | NA | |
| TII_latt_rakenne1234 (%) | 0.471 | |||
| 1 | 4 ( 16.7) | 1 ( 5.3) | ||
| 12 | 2 ( 8.3) | 2 ( 10.5) | ||
| 13 | 18 ( 75.0) | 15 ( 78.9) | ||
| 23 | 0 ( 0.0) | 1 ( 5.3) | ||
| TII_pr_ritila (%) | 0.491 | |||
| 0 | 22 ( 91.7) | 16 ( 84.2) | ||
| 20 | 1 ( 4.2) | 0 ( 0.0) | ||
| 28 | 0 ( 0.0) | 1 ( 5.3) | ||
| 40 | 1 ( 4.2) | 1 ( 5.3) | ||
| 50 | 0 ( 0.0) | 1 ( 5.3) | ||
| TII_pr_viemar = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| TII_kuiv_mat12345 (%) | 0.565 | |||
| 1 | 4 ( 16.7) | 1 ( 5.3) | ||
| 2 | 15 ( 62.5) | 16 ( 84.2) | ||
| 12 | 2 ( 8.3) | 1 ( 5.3) | ||
| 14 | 2 ( 8.3) | 1 ( 5.3) | ||
| 15 | 1 ( 4.2) | 0 ( 0.0) | ||
| TII_kuiv_5_mika = 2 (%) | 1 ( 4.2) | 0 ( 0.0) | 1.000 | |
| TII_maara1234 (%) | 0.508 | |||
| 1 | 3 ( 12.5) | 1 ( 5.3) | ||
| 2 | 4 ( 16.7) | 1 ( 5.3) | ||
| 3 | 13 ( 54.2) | 12 ( 63.2) | ||
| 4 | 4 ( 16.7) | 4 ( 21.1) | ||
| 23 | 0 ( 0.0) | 1 ( 5.3) | ||
| TII_tonkimat_6_mika (%) | 0.186 | |||
| 1 | 23 ( 95.8) | 15 ( 78.9) | ||
| 2 | 1 ( 4.2) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.3) | ||
| 4 | 0 ( 0.0) | 1 ( 5.3) | ||
| 5 | 0 ( 0.0) | 2 ( 10.5) | ||
| TII_lelu1234 (%) | 0.296 | |||
| 2 | 2 ( 8.3) | 0 ( 0.0) | ||
| 4 | 21 ( 87.5) | 18 ( 94.7) | ||
| 5 | 1 ( 4.2) | 0 ( 0.0) | ||
| 24 | 0 ( 0.0) | 1 ( 5.3) | ||
| TII_mat_vaiht = 1 (%) | 24 (100.0) | 18 ( 94.7) | 0.906 | |
| TII_maara123 (%) | 0.877 | |||
| 1 | 3 ( 12.5) | 1 ( 5.3) | ||
| 1.5 | 1 ( 4.2) | 1 ( 5.3) | ||
| 2 | 19 ( 79.2) | 16 ( 84.2) | ||
| 3 | 1 ( 4.2) | 1 ( 5.3) | ||
| TII_annostelu1234 (%) | 0.513 | |||
| 1 | 23 ( 95.8) | 17 ( 89.5) | ||
| 2 | 0 ( 0.0) | 1 ( 5.3) | ||
| 4 | 1 ( 4.2) | 1 ( 5.3) | ||
| TII_lannanpoisto12 (%) | 0.486 | |||
| 1 | 14 ( 58.3) | 9 ( 47.4) | ||
| 2 | 1 ( 4.2) | 0 ( 0.0) | ||
| 3 | 3 ( 12.5) | 2 ( 10.5) | ||
| 4 | 1 ( 4.2) | 0 ( 0.0) | ||
| 5 | 5 ( 20.8) | 8 ( 42.1) | ||
| TII_rak_kunto = 1 (%) | 1 ( 4.2) | 1 ( 5.3) | 1.000 | |
| TII_latt_pitava = 1 (%) | 1 ( 4.2) | 2 ( 10.5) | 0.833 | |
| TII_sairkars = 1 (%) | 23 ( 95.8) | 16 ( 84.2) | 0.439 | |
| TII_ruok_0nonlock_1lock = 1 (%) | 12 ( 50.0) | 5 ( 26.3) | 0.206 | |
| TII_ruokpuht = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| TII_juomalaite123 (%) | 0.358 | |||
| 1 | 23 ( 95.8) | 18 ( 94.7) | ||
| 2 | 1 ( 4.2) | 0 ( 0.0) | ||
| 12 | 0 ( 0.0) | 1 ( 5.3) | ||
| TII_juomapuht = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| TII_juomatoim = 2 (%) | 0 ( 0.0) | 1 ( 5.3) | 0.906 | |
| TII_rauhallisuus123 = 2 (%) | 0 ( 0.0) | 1 ( 5.3) | 0.906 | |
| TII_hoitotarveKE = 2 (%) | 12 ( 50.0) | 10 ( 52.6) | 1.000 | |
| TII_stereo = 1 (%) | 2 ( 8.3) | 2 ( 10.5) | 1.000 | |
| POR_meluton = 1 (%) | 21 ( 87.5) | 12 ( 63.2) | 0.130 | |
| POR_haittael_ei = 1 (%) | 21 ( 87.5) | 16 ( 84.2) | 1.000 | |
| POR_haittael_laatu (%) | 0.561 | |||
| 1 | 14 ( 58.3) | 11 ( 57.9) | ||
| 2 | 1 ( 4.2) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.3) | ||
| 4 | 9 ( 37.5) | 7 ( 36.8) | ||
| POR_ilma_aistin = 1 (%) | 1 ( 4.2) | 0 ( 0.0) | 1.000 | |
| POR_ilma_amm = 1 (%) | 1 ( 4.2) | 0 ( 0.0) | 1.000 | |
| POR_ilma_pöly = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| POR_ilma_muu = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| POR_kosteus = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| POR_valaistus (%) | 0.660 | |||
| 0 | 22 ( 91.7) | 18 ( 94.7) | ||
| 0.5 | 1 ( 4.2) | 0 ( 0.0) | ||
| 1 | 1 ( 4.2) | 1 ( 5.3) | ||
| POR_latt_rakenne1234 (%) | 0.164 | |||
| 1 | 2 ( 8.3) | 0 ( 0.0) | ||
| 2 | 3 ( 12.5) | 0 ( 0.0) | ||
| 12 | 18 ( 75.0) | 18 ( 94.7) | ||
| 13 | 0 ( 0.0) | 1 ( 5.3) | ||
| 123 | 1 ( 4.2) | 0 ( 0.0) | ||
| POR_pr_rako = 38 (%) | 0 ( 0.0) | 1 ( 5.3) | 0.906 | |
| POR_maara1234 (%) | 0.265 | |||
| 2 | 5 ( 20.8) | 3 ( 15.8) | ||
| 3 | 14 ( 58.3) | 15 ( 78.9) | ||
| 4 | 5 ( 20.8) | 1 ( 5.3) | ||
| POR_tonkimat_6_mika (%) | 0.081 | |||
| 1 | 22 ( 91.7) | 14 ( 73.7) | ||
| 2 | 0 ( 0.0) | 1 ( 5.3) | ||
| 3 | 1 ( 4.2) | 0 ( 0.0) | ||
| 4 | 0 ( 0.0) | 4 ( 21.1) | ||
| 5 | 1 ( 4.2) | 0 ( 0.0) | ||
| POR_lelu1234 (%) | 0.643 | |||
| 2 | 1 ( 4.2) | 0 ( 0.0) | ||
| 3 | 1 ( 4.2) | 1 ( 5.3) | ||
| 4 | 21 ( 87.5) | 18 ( 94.7) | ||
| 5 | 1 ( 4.2) | 0 ( 0.0) | ||
| POR_lelukomm (%) | 0.414 | |||
| 1 | 22 ( 91.7) | 18 ( 94.7) | ||
| 2 | 1 ( 4.2) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.3) | ||
| 4 | 1 ( 4.2) | 0 ( 0.0) | ||
| POR_mat_vaiht = 2 (%) | 0 ( 0.0) | 1 ( 5.3) | 0.906 | |
| POR_maara123 (%) | 0.433 | |||
| 1 | 2 ( 8.3) | 0 ( 0.0) | ||
| 2 | 21 ( 87.5) | 18 ( 94.7) | ||
| 3 | 1 ( 4.2) | 1 ( 5.3) | ||
| POR_annostelu1234 (%) | 0.386 | |||
| 1 | 20 ( 83.3) | 17 ( 89.5) | ||
| 2 | 2 ( 8.3) | 1 ( 5.3) | ||
| 3 | 2 ( 8.3) | 0 ( 0.0) | ||
| 4 | 0 ( 0.0) | 1 ( 5.3) | ||
| POR_lannanpoisto12 = 2 (%) | 21 ( 87.5) | 18 ( 94.7) | 0.777 | |
| POR_rak_kunto = 1 (%) | 2 ( 8.3) | 1 ( 5.3) | 1.000 | |
| POR_latt_pitava = 1 (%) | 3 ( 12.5) | 0 ( 0.0) | 0.320 | |
| POR_sairkars (%) | 0.679 | |||
| 1 | 17 ( 70.8) | 12 ( 63.2) | ||
| 2 | 1 ( 4.2) | 0 ( 0.0) | ||
| 3 | 2 ( 8.3) | 2 ( 10.5) | ||
| 4 | 4 ( 16.7) | 4 ( 21.1) | ||
| 5 | 0 ( 0.0) | 1 ( 5.3) | ||
| POR_ruoklaite12345 (%) | 0.554 | |||
| 2 | 1 ( 4.2) | 1 ( 5.3) | ||
| 2.5 | 22 ( 91.7) | 17 ( 89.5) | ||
| 3 | 1 ( 4.2) | 0 ( 0.0) | ||
| 25 | 0 ( 0.0) | 1 ( 5.3) | ||
| POR_ruokpaikka = 1 (%) | 24 (100.0) | 19 (100.0) | NA | |
| POR_ruokpuht = 1 (%) | 2 ( 8.3) | 1 ( 5.3) | 1.000 | |
| POR_juomalaite123 = 13 (%) | 0 ( 0.0) | 1 ( 5.3) | 0.906 | |
| POR_juonalkm = 1 (%) | 24 (100.0) | 19 (100.0) | NA | |
| POR_juomapuht = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| POR_juomatoim = 1 (%) | 0 ( 0.0) | 1 ( 5.3) | 0.906 | |
| POR_rauhallisuus123 = 1 (%) | 24 (100.0) | 19 (100.0) | NA | |
| Hajukarjut_per_emakko (%) | 0.564 | |||
| 0 | 3 ( 12.5) | 4 ( 21.1) | ||
| 0.01 | 14 ( 58.3) | 11 ( 57.9) | ||
| 0.02 | 4 ( 16.7) | 1 ( 5.3) | ||
| 0.03 | 3 ( 12.5) | 2 ( 10.5) | ||
| 0.06 | 0 ( 0.0) | 1 ( 5.3) | ||
| TII_VIRMaa_0_ei_1pellel_2pelvir_3niukuihiemnvir_4riirunkuiv (%) | 0.148 | |||
| 0 | 1 ( 4.2) | 3 ( 15.8) | ||
| 1 | 0 ( 0.0) | 2 ( 10.5) | ||
| 2 | 4 ( 16.7) | 4 ( 21.1) | ||
| 3 | 5 ( 20.8) | 5 ( 26.3) | ||
| 4 | 14 ( 58.3) | 5 ( 26.3) | ||
| TII_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme (%) | 0.214 | |||
| 0 | 1 ( 4.2) | 3 ( 15.8) | ||
| 1 | 5 ( 20.8) | 4 ( 21.1) | ||
| 2 | 13 ( 54.2) | 5 ( 26.3) | ||
| 3 | 5 ( 20.8) | 7 ( 36.8) | ||
| AS_VIRMaa_0ei_1pellel_2pelvir_3niukuihiemvir_4riirunkuiv (%) | 0.052 | |||
| 0 | 5 ( 20.8) | 0 ( 0.0) | ||
| 1 | 1 ( 4.2) | 6 ( 31.6) | ||
| 2 | 9 ( 37.5) | 8 ( 42.1) | ||
| 3 | 6 ( 25.0) | 3 ( 15.8) | ||
| 4 | 3 ( 12.5) | 2 ( 10.5) | ||
| AS_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme (%) | 0.065 | |||
| 0 | 5 ( 20.8) | 0 ( 0.0) | ||
| 1 | 9 ( 37.5) | 13 ( 68.4) | ||
| 2 | 6 ( 25.0) | 2 ( 10.5) | ||
| 3 | 4 ( 16.7) | 4 ( 21.1) | ||
| POR_VIRMaa_0_ei_1pellel_2pelvir_3niukui_4riikuiv (%) | 0.320 | |||
| 0 | 2 ( 8.3) | 1 ( 5.3) | ||
| 1 | 0 ( 0.0) | 3 ( 15.8) | ||
| 2 | 6 ( 25.0) | 3 ( 15.8) | ||
| 3 | 9 ( 37.5) | 8 ( 42.1) | ||
| 4 | 7 ( 29.2) | 4 ( 21.1) | ||
| POR_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme (%) | 0.415 | |||
| 0 | 1 ( 4.2) | 1 ( 5.3) | ||
| 1 | 4 ( 16.7) | 5 ( 26.3) | ||
| 2 | 18 ( 75.0) | 10 ( 52.6) | ||
| 3 | 1 ( 4.2) | 3 ( 15.8) | ||
| Koulmax_1peru_2ops_3a_4amk_5yl (%) | 0.833 | |||
| 2 | 2 ( 8.3) | 3 ( 15.8) | ||
| 3 | 16 ( 66.7) | 12 ( 63.2) | ||
| 4 | 4 ( 16.7) | 2 ( 10.5) | ||
| 5 | 2 ( 8.3) | 2 ( 10.5) | ||
| Stressi_1erpal_4jnkv (%) | 0.395 | |||
| 1 | 5 ( 20.8) | 1 ( 5.3) | ||
| 2 | 3 ( 12.5) | 5 ( 26.3) | ||
| 3 | 8 ( 33.3) | 6 ( 31.6) | ||
| 4 | 8 ( 33.3) | 7 ( 36.8) | ||
| EMKUOLLJAKO = 1 (%) | 6 ( 25.0) | 14 ( 73.7) | 0.004 | |
| EMPOISJAKO = 1 (%) | 0 ( 0.0) | 19 (100.0) | <0.001 | |
| EMENKUOLLJAKO = 1 (%) | 5 ( 20.8) | 13 ( 68.4) | 0.005 | |
| EMENPOISJAKO = 1 (%) | 1 ( 4.2) | 15 ( 78.9) | <0.001 | |
| NIVEL_01 = 2 (%) | 9 ( 37.5) | 9 ( 47.4) | 0.734 | |
| PAISE_01 = 2 (%) | 9 ( 37.5) | 9 ( 47.4) | 0.734 | |
| MAKUU01 = 2 (%) | 9 ( 37.5) | 9 ( 47.4) | 0.734 | |
| KOKO_01 = 2 (%) | 8 ( 33.3) | 10 ( 52.6) | 0.336 | |
| OSA_01 = 2 (%) | 10 ( 41.7) | 8 ( 42.1) | 1.000 | |
| JOKUHYLK_01 = 2 (%) | 8 ( 33.3) | 10 ( 52.6) | 0.336 | |
| PLEUR_01 = 1 (%) | 6 ( 25.0) | 6 ( 31.6) | 0.892 | |
| PNEUM_01 = 2 (%) | 8 ( 33.3) | 10 ( 52.6) | 0.336 | |
| SAIRKARS_AST_TII = 1 (%) | 18 ( 75.0) | 15 ( 78.9) | 1.000 | |
| EMKUOL (mean (sd)) | 0.25 (0.44) | 0.74 (0.45) | 0.001 | |
| EMPOIS (mean (sd)) | 0.00 (0.00) | 1.00 (0.00) | <0.001 |
MCA
tilatkat<-tilat[,1:218]%>%mutate_all(as.factor)
tilatnum<-tilat[,219:233]%>%mutate_all(as.numeric)
tilat<-cbind(tilatkat,tilatnum)
res_mca = MCA(tilat, quanti.sup = c(219:233), graph = FALSE) To visualize the percentage of inertia explained by each MCA dimension:
eig.val <- res_mca$eig
barplot(eig.val[, 2],
names.arg = 1:nrow(eig.val),
main = "Variances Explained by Dimensions (%)",
xlab = "Principal Dimensions",
ylab = "Percentage of variances",
col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2],
type = "b", pch = 19, col = "red")
fviz_mca_var(res_mca, choice = "mca.cor",
repel = TRUE, # Avoid text overlapping (slow)
ggtheme = theme_minimal())
To visualize the percentage of inertia explained by each MCA dimension:
fviz_mca_var(res_mca, col.var = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # avoid text overlapping (slow)
ggtheme = theme_minimal()
)
# load data
setwd("~/GitHub/tilataso")
library(readr)
tilapieni<-read.csv(file="tilapieni.csv", header=TRUE)Kuvailevat
Valitsen muutaman jatkuvan muuttujan ja muutoin valitsen ne, joissa on alle 6 kategoriaa. Yhteenveto muuttujista:
tilapienikat<-tilapieni[1:76]%>%mutate_all(as.factor)
tilapieninum<-tilapieni[77:92]%>%mutate_all(as.numeric)
tilapieni<-cbind(tilapieninum,tilapienikat)
summaryKable(tilapieni) %>%
kable("html", align = "rrr", caption = "Data variable summary") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px")| Min | 1st Q | Median | Mean | 3rd Q | Max | |
|---|---|---|---|---|---|---|
| Karjut_astsiem | 0.000 | 0.000 | 0.000 | 0.419 | 0.000 | 6.000 |
| emakot | 37.000 | 102.500 | 270.000 | 428.837 | 635.000 | 2100.000 |
| ensikot | 0.000 | 17.000 | 30.000 | 70.930 | 76.500 | 710.000 |
| lihasiat | 0.000 | 0.000 | 40.000 | 381.512 | 390.000 | 3000.000 |
| karjut | 1.000 | 2.000 | 2.000 | 2.907 | 4.000 | 7.000 |
| kokemusave | 3.000 | 11.000 | 16.500 | 17.802 | 25.000 | 40.000 |
| kokemusmax | 5.000 | 20.000 | 25.000 | 25.349 | 30.000 | 47.000 |
| emakoitaper | 10.000 | 60.000 | 100.000 | 103.765 | 137.915 | 350.000 |
| nivelpros | 0.000 | 1.540 | 2.100 | 2.695 | 3.440 | 13.330 |
| paisepros | 0.000 | 4.460 | 6.800 | 6.886 | 8.885 | 16.280 |
| keuhtulpros | 0.000 | 0.000 | 0.920 | 1.004 | 1.475 | 3.590 |
| keuhkopros | 0.000 | 0.965 | 1.700 | 7.626 | 10.625 | 36.360 |
| kokopros | 0.000 | 0.735 | 1.360 | 1.808 | 2.185 | 7.250 |
| osapros | 0.000 | 8.080 | 11.210 | 11.908 | 15.325 | 34.620 |
| emkuol | 0.000 | 5.540 | 8.700 | 9.840 | 13.900 | 26.760 |
| empoisp | 25.730 | 42.710 | 47.610 | 52.106 | 58.340 | 120.600 |
| Haastrooli_1OmEiosall_2OmOsall_3Esimies | Levels | 1: 10 | 2: 28 | 3: 5 | – | – |
| Tuotsuunta | Levels | 1: 22 | 2: 21 | – | – | – |
| Tautsu_012 | Levels | 0: 13 | 1: 13 | 2: 17 | – | – |
| PORSOSASTO_kertayt_0ei | Levels | 0: 25 | 1: 18 | – | – | – |
| PORS_pesu_0ei | Levels | 0: 10 | 1: 33 | – | – | – |
| PORS_desinf_0ei_1LIU_2KUIVA | Levels | 0: 9 | 1: 19 | 2: 10 | 12: 5 | – |
| PORS_tyhjana_mi1vr_0ei | Levels | 0: 17 | 1: 26 | – | – | – |
| Tuhoelmerkkeja_0kylla_1ei | Levels | 0: 33 | 1: 10 | – | – | – |
| kissoja0on1ei | Levels | 0: 26 | 0.5: 2 | 1: 15 | – | – |
| Kotielain_sikalaan_0kylla_1ei | Levels | 0: 9 | 1: 34 | – | – | – |
| Vesi_1kunn_0oma | Levels | 0: 16 | 1: 27 | – | – | – |
| ClC | Levels | 0: 40 | 1: 3 | – | – | – |
| ClA | Levels | 0: 43 | – | – | – | – |
| SI | Levels | 0: 39 | 1: 4 | – | – | – |
| APP | Levels | 0: 38 | 1: 5 | – | – | – |
| Loisaika_1ennenpors_2_porskars | Levels | 1: 27 | 2: 16 | – | – | – |
| Ton_tiheys_1aina_2jaetaan | Levels | 1: 39 | 2: 4 | – | – | – |
| Muutelkaynn_0ei_1_satunn_2kaynnmuusaann | Levels | 0: 16 | 1: 21 | 2: 6 | – | – |
| maitokuume | Levels | 0: 21 | 1: 22 | – | – | – |
| metriitti | Levels | 0: 24 | 1: 19 | – | – | – |
| valuttelu | Levels | 0: 38 | 1: 5 | – | – | – |
| mastiitti | Levels | 0: 33 | 1: 10 | – | – | – |
| ontuma | Levels | 0: 12 | 1: 31 | – | – | – |
| syomattomyys | Levels | 0: 21 | 1: 22 | – | – | – |
| kuume | Levels | 0: 37 | 1: 6 | – | – | – |
| loukkaantuminen | Levels | 0: 27 | 1: 16 | – | – | – |
| AB_rutiinilaak | Levels | 0: 37 | 1: 6 | – | – | – |
| Oksitosiini_rutiinisti | Levels | 0: 26 | 1: 17 | – | – | – |
| Kaynnistys_rutiinisti | Levels | 0: 39 | 1: 4 | – | – | – |
| NSAID_porsituksessa_rutiini | Levels | 0: 33 | 1: 10 | – | – | – |
| OMATENSIKOT_0EI_1KYLLa | Levels | 0: 15 | 1: 28 | – | – | – |
| Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk | Levels | 1: 1 | 2: 5 | 3: 35 | 4: 2 | – |
| Kiimantark_ryhmakaytt | Levels | 0: 5 | 1: 38 | – | – | – |
| Kiimantarkalkaa_vrkvier | Levels | 0: 12 | 1: 23 | 3: 3 | 4: 1 | 5: 4 |
| Kiimamerk_emakonselka | Levels | 0: 6 | 1: 37 | – | – | – |
| Kiimantark_postsiem | Levels | 0: 2 | 1: 41 | – | – | – |
| Postsiem_ryhmakaytt_havainnointi | Levels | 0: 5 | 1: 38 | – | – | – |
| Tiin_ultra2 | Levels | 6: 41 | 8: 1 | 10: 1 | – | – |
| Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen | Levels | 0: 16 | 1: 2 | 2: 8 | 3: 2 | 4: 15 |
| Pesantekomatmaara_1runsas_2jnkv_3niukka | Levels | 1: 3 | 2: 33 | 3: 7 | – | – |
| PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa | Levels | 1: 8 | 2: 35 | – | – | – |
| AS_maara123 | Levels | 0: 1 | 2: 37 | 3: 5 | – | – |
| AS_annostelu1234 | Levels | 0: 2 | 1: 40 | 3: 1 | – | – |
| AS_sairkars | Levels | 0: 32 | 1: 11 | – | – | – |
| AS_ruoklaite12345 | Levels | 0: 2 | 4: 41 | – | – | – |
| AS_ruokpaikka | Levels | 0: 1 | 1: 41 | 4: 1 | – | – |
| TII_alusta12345 | Levels | 1: 43 | – | – | – | – |
| TII_latt_rakenne1234 | Levels | 1: 5 | 12: 4 | 13: 33 | 23: 1 | – |
| TII_kuiv_mat12345 | Levels | 1: 5 | 2: 31 | 12: 3 | 14: 3 | 15: 1 |
| TII_maara1234 | Levels | 1: 4 | 2: 5 | 3: 25 | 4: 8 | 23: 1 |
| TII_tonkimat_6_mika | Levels | 1: 38 | 2: 1 | 3: 1 | 4: 1 | 5: 2 |
| TII_lelu1234 | Levels | 2: 2 | 4: 39 | 5: 1 | 24: 1 | – |
| TII_maara123 | Levels | 1: 4 | 1.5: 2 | 2: 35 | 3: 2 | – |
| TII_annostelu1234 | Levels | 1: 40 | 2: 1 | 4: 2 | – | – |
| TII_sairkars | Levels | 0: 4 | 1: 39 | – | – | – |
| POR_latt_rakenne1234 | Levels | 1: 2 | 2: 3 | 12: 36 | 13: 1 | 123: 1 |
| POR_maara1234 | Levels | 2: 8 | 3: 29 | 4: 6 | – | – |
| POR_tonkimat_6_mika | Levels | 1: 36 | 2: 1 | 3: 1 | 4: 4 | 5: 1 |
| POR_lelu1234 | Levels | 2: 1 | 3: 2 | 4: 39 | 5: 1 | – |
| POR_mat_vaiht | Levels | 1: 42 | 2: 1 | – | – | – |
| POR_maara123 | Levels | 1: 2 | 2: 39 | 3: 2 | – | – |
| POR_annostelu1234 | Levels | 1: 37 | 2: 3 | 3: 2 | 4: 1 | – |
| Koulmax_1peru_2ops_3a_4amk_5yl | Levels | 2: 5 | 3: 28 | 4: 6 | 5: 4 | – |
| Stressi_1erpal_4jnkv | Levels | 1: 6 | 2: 8 | 3: 14 | 4: 15 | – |
| EMKUOLLJAKO | Levels | 0: 23 | 1: 20 | – | – | – |
| EMPOISJAKO | Levels | 0: 24 | 1: 19 | – | – | – |
| EMENKUOLLJAKO | Levels | 0: 25 | 1: 18 | – | – | – |
| EMENPOISJAKO | Levels | 0: 27 | 1: 16 | – | – | – |
| NIVEL_01 | Levels | 1: 25 | 2: 18 | – | – | – |
| MAKUU01 | Levels | 1: 25 | 2: 18 | – | – | – |
| KOKO_01 | Levels | 1: 25 | 2: 18 | – | – | – |
| OSA_01 | Levels | 1: 25 | 2: 18 | – | – | – |
| JOKUHYLK_01 | Levels | 1: 25 | 2: 18 | – | – | – |
| PLEUR_01 | Levels | 0: 31 | 1: 12 | – | – | – |
| PNEUM_01 | Levels | 1: 25 | 2: 18 | – | – | – |
| SAIRKARS_AST_TII | Levels | 0: 10 | 1: 33 | – | – | – |
kuvat2<-tilapienikat
colnames(kuvat2)## [1] "Haastrooli_1OmEiosall_2OmOsall_3Esimies"
## [2] "Tuotsuunta"
## [3] "Tautsu_012"
## [4] "PORSOSASTO_kertayt_0ei"
## [5] "PORS_pesu_0ei"
## [6] "PORS_desinf_0ei_1LIU_2KUIVA"
## [7] "PORS_tyhjana_mi1vr_0ei"
## [8] "Tuhoelmerkkeja_0kylla_1ei"
## [9] "kissoja0on1ei"
## [10] "Kotielain_sikalaan_0kylla_1ei"
## [11] "Vesi_1kunn_0oma"
## [12] "ClC"
## [13] "ClA"
## [14] "SI"
## [15] "APP"
## [16] "Loisaika_1ennenpors_2_porskars"
## [17] "Ton_tiheys_1aina_2jaetaan"
## [18] "Muutelkaynn_0ei_1_satunn_2kaynnmuusaann"
## [19] "maitokuume"
## [20] "metriitti"
## [21] "valuttelu"
## [22] "mastiitti"
## [23] "ontuma"
## [24] "syomattomyys"
## [25] "kuume"
## [26] "loukkaantuminen"
## [27] "AB_rutiinilaak"
## [28] "Oksitosiini_rutiinisti"
## [29] "Kaynnistys_rutiinisti"
## [30] "NSAID_porsituksessa_rutiini"
## [31] "OMATENSIKOT_0EI_1KYLLa"
## [32] "Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk"
## [33] "Kiimantark_ryhmakaytt"
## [34] "Kiimantarkalkaa_vrkvier"
## [35] "Kiimamerk_emakonselka"
## [36] "Kiimantark_postsiem"
## [37] "Postsiem_ryhmakaytt_havainnointi"
## [38] "Tiin_ultra2"
## [39] "Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen"
## [40] "Pesantekomatmaara_1runsas_2jnkv_3niukka"
## [41] "PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa"
## [42] "AS_maara123"
## [43] "AS_annostelu1234"
## [44] "AS_sairkars"
## [45] "AS_ruoklaite12345"
## [46] "AS_ruokpaikka"
## [47] "TII_alusta12345"
## [48] "TII_latt_rakenne1234"
## [49] "TII_kuiv_mat12345"
## [50] "TII_maara1234"
## [51] "TII_tonkimat_6_mika"
## [52] "TII_lelu1234"
## [53] "TII_maara123"
## [54] "TII_annostelu1234"
## [55] "TII_sairkars"
## [56] "POR_latt_rakenne1234"
## [57] "POR_maara1234"
## [58] "POR_tonkimat_6_mika"
## [59] "POR_lelu1234"
## [60] "POR_mat_vaiht"
## [61] "POR_maara123"
## [62] "POR_annostelu1234"
## [63] "Koulmax_1peru_2ops_3a_4amk_5yl"
## [64] "Stressi_1erpal_4jnkv"
## [65] "EMKUOLLJAKO"
## [66] "EMPOISJAKO"
## [67] "EMENKUOLLJAKO"
## [68] "EMENPOISJAKO"
## [69] "NIVEL_01"
## [70] "MAKUU01"
## [71] "KOKO_01"
## [72] "OSA_01"
## [73] "JOKUHYLK_01"
## [74] "PLEUR_01"
## [75] "PNEUM_01"
## [76] "SAIRKARS_AST_TII"gather(kuvat2) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="purple") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+ scale_fill_manual("key")
kor2<-tilapieninum
cor_fun <- function(data, mapping, method="pearson", ndp=2, sz=5, stars=TRUE, ...){
data <- na.omit(data[,c(as.character(mapping$x), as.character(mapping$y))])
x <- data[,as.character(mapping$x)]
y <- data[,as.character(mapping$y)]
corr <- cor.test(x, y, method=method)
est <- corr$estimate
lb.size <- sz* abs(est)
if(stars){
stars <- c("***", "**", "*", "")[findInterval(corr$p.value, c(0, 0.001, 0.01, 0.05, 1))]
lbl <- paste0(round(est, ndp), stars)
}else{
lbl <- round(est, ndp)
}
ggplot(data=data, mapping=mapping) +
annotate("text", x=mean(x), y=mean(y), label=lbl, size=lb.size,...)+
theme(panel.grid = element_blank())
}
ggpairs(kor2%>%mutate_all(as.numeric),
lower=list(continuous=wrap("smooth", colour="purple")),
diag=list(continuous=wrap("barDiag", fill="purple")),
upper=list(continuous=cor_fun),title="Graphical overview of the 17 variables")
KreateTableOne = function(x, ...){
t1 = tableone::CreateTableOne(data=x, ...)
t2 = print(t1, quote=TRUE)
rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
return(t2)
}Yhteenveto kuolleisuuden mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table1 = KreateTableOne(x=tilapieni, factorVars=colnames(tilapienikat), strata='EMKUOLLJAKO')table1%>%
kable("html", align = "rrr", caption = "Data variable summary strat by EMKUOL") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 23 | 20 | ||
| Karjut_astsiem (mean (sd)) | 0.13 (0.46) | 0.75 (1.92) | 0.140 | |
| emakot (mean (sd)) | 305.26 (291.79) | 570.95 (509.87) | 0.039 | |
| ensikot (mean (sd)) | 41.52 (45.16) | 104.75 (174.17) | 0.101 | |
| lihasiat (mean (sd)) | 406.13 (718.12) | 353.20 (773.30) | 0.817 | |
| karjut (mean (sd)) | 2.43 (1.59) | 3.45 (1.90) | 0.064 | |
| kokemusave (mean (sd)) | 21.07 (10.17) | 14.05 (7.17) | 0.014 | |
| kokemusmax (mean (sd)) | 27.39 (11.19) | 23.00 (9.22) | 0.172 | |
| emakoitaper (mean (sd)) | 88.01 (46.20) | 121.88 (74.77) | 0.077 | |
| nivelpros (mean (sd)) | 2.23 (2.37) | 3.23 (2.68) | 0.202 | |
| paisepros (mean (sd)) | 6.45 (4.38) | 7.39 (4.14) | 0.478 | |
| keuhtulpros (mean (sd)) | 0.87 (0.92) | 1.16 (0.92) | 0.315 | |
| keuhkopros (mean (sd)) | 4.00 (7.16) | 11.80 (13.39) | 0.020 | |
| kokopros (mean (sd)) | 1.35 (1.37) | 2.34 (2.08) | 0.070 | |
| osapros (mean (sd)) | 11.59 (8.27) | 12.28 (5.34) | 0.752 | |
| emkuol (mean (sd)) | 5.43 (2.52) | 14.91 (4.55) | <0.001 | |
| empoisp (mean (sd)) | 44.45 (12.12) | 60.90 (18.90) | 0.001 | |
| Haastrooli_1OmEiosall_2OmOsall_3Esimies (%) | 0.431 | |||
| 1 | 4 ( 17.4) | 6 ( 30.0) | ||
| 2 | 17 ( 73.9) | 11 ( 55.0) | ||
| 3 | 2 ( 8.7) | 3 ( 15.0) | ||
| Tuotsuunta = 2 (%) | 14 ( 60.9) | 7 ( 35.0) | 0.165 | |
| Tautsu_012 (%) | 0.733 | |||
| 0 | 8 ( 34.8) | 5 ( 25.0) | ||
| 1 | 7 ( 30.4) | 6 ( 30.0) | ||
| 2 | 8 ( 34.8) | 9 ( 45.0) | ||
| PORSOSASTO_kertayt_0ei = 1 (%) | 9 ( 39.1) | 9 ( 45.0) | 0.937 | |
| PORS_pesu_0ei = 1 (%) | 17 ( 73.9) | 16 ( 80.0) | 0.913 | |
| PORS_desinf_0ei_1LIU_2KUIVA (%) | 0.623 | |||
| 0 | 5 ( 21.7) | 4 ( 20.0) | ||
| 1 | 9 ( 39.1) | 10 ( 50.0) | ||
| 2 | 7 ( 30.4) | 3 ( 15.0) | ||
| 12 | 2 ( 8.7) | 3 ( 15.0) | ||
| PORS_tyhjana_mi1vr_0ei = 1 (%) | 13 ( 56.5) | 13 ( 65.0) | 0.799 | |
| Tuhoelmerkkeja_0kylla_1ei = 1 (%) | 5 ( 21.7) | 5 ( 25.0) | 1.000 | |
| kissoja0on1ei (%) | 0.005 | |||
| 0 | 19 ( 82.6) | 7 ( 35.0) | ||
| 0.5 | 0 ( 0.0) | 2 ( 10.0) | ||
| 1 | 4 ( 17.4) | 11 ( 55.0) | ||
| Kotielain_sikalaan_0kylla_1ei = 1 (%) | 17 ( 73.9) | 17 ( 85.0) | 0.606 | |
| Vesi_1kunn_0oma = 1 (%) | 16 ( 69.6) | 11 ( 55.0) | 0.503 | |
| ClC = 1 (%) | 1 ( 4.3) | 2 ( 10.0) | 0.900 | |
| ClA = 0 (%) | 23 (100.0) | 20 (100.0) | NA | |
| SI = 1 (%) | 1 ( 4.3) | 3 ( 15.0) | 0.501 | |
| APP = 1 (%) | 3 ( 13.0) | 2 ( 10.0) | 1.000 | |
| Loisaika_1ennenpors_2_porskars = 2 (%) | 9 ( 39.1) | 7 ( 35.0) | 1.000 | |
| Ton_tiheys_1aina_2jaetaan = 2 (%) | 4 ( 17.4) | 0 ( 0.0) | 0.152 | |
| Muutelkaynn_0ei_1_satunn_2kaynnmuusaann (%) | 0.142 | |||
| 0 | 10 ( 43.5) | 6 ( 30.0) | ||
| 1 | 12 ( 52.2) | 9 ( 45.0) | ||
| 2 | 1 ( 4.3) | 5 ( 25.0) | ||
| maitokuume = 1 (%) | 12 ( 52.2) | 10 ( 50.0) | 1.000 | |
| metriitti = 1 (%) | 10 ( 43.5) | 9 ( 45.0) | 1.000 | |
| valuttelu = 1 (%) | 2 ( 8.7) | 3 ( 15.0) | 0.868 | |
| mastiitti = 1 (%) | 4 ( 17.4) | 6 ( 30.0) | 0.539 | |
| ontuma = 1 (%) | 15 ( 65.2) | 16 ( 80.0) | 0.461 | |
| syomattomyys = 1 (%) | 10 ( 43.5) | 12 ( 60.0) | 0.438 | |
| kuume = 1 (%) | 2 ( 8.7) | 4 ( 20.0) | 0.531 | |
| loukkaantuminen = 1 (%) | 10 ( 43.5) | 6 ( 30.0) | 0.551 | |
| AB_rutiinilaak = 1 (%) | 2 ( 8.7) | 4 ( 20.0) | 0.531 | |
| Oksitosiini_rutiinisti = 1 (%) | 8 ( 34.8) | 9 ( 45.0) | 0.711 | |
| Kaynnistys_rutiinisti = 1 (%) | 0 ( 0.0) | 4 ( 20.0) | 0.084 | |
| NSAID_porsituksessa_rutiini = 1 (%) | 6 ( 26.1) | 4 ( 20.0) | 0.913 | |
| OMATENSIKOT_0EI_1KYLLa = 1 (%) | 15 ( 65.2) | 13 ( 65.0) | 1.000 | |
| Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk (%) | 0.386 | |||
| 1 | 1 ( 4.3) | 0 ( 0.0) | ||
| 2 | 3 ( 13.0) | 2 ( 10.0) | ||
| 3 | 17 ( 73.9) | 18 ( 90.0) | ||
| 4 | 2 ( 8.7) | 0 ( 0.0) | ||
| Kiimantark_ryhmakaytt = 1 (%) | 20 ( 87.0) | 18 ( 90.0) | 1.000 | |
| Kiimantarkalkaa_vrkvier (%) | 0.264 | |||
| 0 | 7 ( 30.4) | 5 ( 25.0) | ||
| 1 | 14 ( 60.9) | 9 ( 45.0) | ||
| 3 | 0 ( 0.0) | 3 ( 15.0) | ||
| 4 | 0 ( 0.0) | 1 ( 5.0) | ||
| 5 | 2 ( 8.7) | 2 ( 10.0) | ||
| Kiimamerk_emakonselka = 1 (%) | 17 ( 73.9) | 20 (100.0) | 0.043 | |
| Kiimantark_postsiem = 1 (%) | 21 ( 91.3) | 20 (100.0) | 0.532 | |
| Postsiem_ryhmakaytt_havainnointi = 1 (%) | 20 ( 87.0) | 18 ( 90.0) | 1.000 | |
| Tiin_ultra2 (%) | 0.364 | |||
| 6 | 22 ( 95.7) | 19 ( 95.0) | ||
| 8 | 1 ( 4.3) | 0 ( 0.0) | ||
| 10 | 0 ( 0.0) | 1 ( 5.0) | ||
| Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen (%) | 0.115 | |||
| 0 | 10 ( 43.5) | 6 ( 30.0) | ||
| 1 | 0 ( 0.0) | 2 ( 10.0) | ||
| 2 | 2 ( 8.7) | 6 ( 30.0) | ||
| 3 | 2 ( 8.7) | 0 ( 0.0) | ||
| 4 | 9 ( 39.1) | 6 ( 30.0) | ||
| Pesantekomatmaara_1runsas_2jnkv_3niukka (%) | 0.763 | |||
| 1 | 1 ( 4.3) | 2 ( 10.0) | ||
| 2 | 18 ( 78.3) | 15 ( 75.0) | ||
| 3 | 4 ( 17.4) | 3 ( 15.0) | ||
| PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa = 2 (%) | 19 ( 82.6) | 16 ( 80.0) | 1.000 | |
| AS_maara123 (%) | 0.054 | |||
| 0 | 0 ( 0.0) | 1 ( 5.0) | ||
| 2 | 18 ( 78.3) | 19 ( 95.0) | ||
| 3 | 5 ( 21.7) | 0 ( 0.0) | ||
| AS_annostelu1234 (%) | 0.639 | |||
| 0 | 1 ( 4.3) | 1 ( 5.0) | ||
| 1 | 21 ( 91.3) | 19 ( 95.0) | ||
| 3 | 1 ( 4.3) | 0 ( 0.0) | ||
| AS_sairkars = 1 (%) | 3 ( 13.0) | 8 ( 40.0) | 0.095 | |
| AS_ruoklaite12345 = 4 (%) | 22 ( 95.7) | 19 ( 95.0) | 1.000 | |
| AS_ruokpaikka (%) | 0.402 | |||
| 0 | 1 ( 4.3) | 0 ( 0.0) | ||
| 1 | 21 ( 91.3) | 20 (100.0) | ||
| 4 | 1 ( 4.3) | 0 ( 0.0) | ||
| TII_alusta12345 = 1 (%) | 23 (100.0) | 20 (100.0) | NA | |
| TII_latt_rakenne1234 (%) | 0.624 | |||
| 1 | 2 ( 8.7) | 3 ( 15.0) | ||
| 12 | 2 ( 8.7) | 2 ( 10.0) | ||
| 13 | 19 ( 82.6) | 14 ( 70.0) | ||
| 23 | 0 ( 0.0) | 1 ( 5.0) | ||
| TII_kuiv_mat12345 (%) | 0.508 | |||
| 1 | 4 ( 17.4) | 1 ( 5.0) | ||
| 2 | 15 ( 65.2) | 16 ( 80.0) | ||
| 12 | 1 ( 4.3) | 2 ( 10.0) | ||
| 14 | 2 ( 8.7) | 1 ( 5.0) | ||
| 15 | 1 ( 4.3) | 0 ( 0.0) | ||
| TII_maara1234 (%) | 0.669 | |||
| 1 | 3 ( 13.0) | 1 ( 5.0) | ||
| 2 | 2 ( 8.7) | 3 ( 15.0) | ||
| 3 | 14 ( 60.9) | 11 ( 55.0) | ||
| 4 | 4 ( 17.4) | 4 ( 20.0) | ||
| 23 | 0 ( 0.0) | 1 ( 5.0) | ||
| TII_tonkimat_6_mika (%) | 0.440 | |||
| 1 | 22 ( 95.7) | 16 ( 80.0) | ||
| 2 | 0 ( 0.0) | 1 ( 5.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.0) | ||
| 4 | 0 ( 0.0) | 1 ( 5.0) | ||
| 5 | 1 ( 4.3) | 1 ( 5.0) | ||
| TII_lelu1234 (%) | 0.257 | |||
| 2 | 2 ( 8.7) | 0 ( 0.0) | ||
| 4 | 21 ( 91.3) | 18 ( 90.0) | ||
| 5 | 0 ( 0.0) | 1 ( 5.0) | ||
| 24 | 0 ( 0.0) | 1 ( 5.0) | ||
| TII_maara123 (%) | 0.844 | |||
| 1 | 3 ( 13.0) | 1 ( 5.0) | ||
| 1.5 | 1 ( 4.3) | 1 ( 5.0) | ||
| 2 | 18 ( 78.3) | 17 ( 85.0) | ||
| 3 | 1 ( 4.3) | 1 ( 5.0) | ||
| TII_annostelu1234 (%) | 0.550 | |||
| 1 | 22 ( 95.7) | 18 ( 90.0) | ||
| 2 | 0 ( 0.0) | 1 ( 5.0) | ||
| 4 | 1 ( 4.3) | 1 ( 5.0) | ||
| TII_sairkars = 1 (%) | 21 ( 91.3) | 18 ( 90.0) | 1.000 | |
| POR_latt_rakenne1234 (%) | 0.387 | |||
| 1 | 2 ( 8.7) | 0 ( 0.0) | ||
| 2 | 2 ( 8.7) | 1 ( 5.0) | ||
| 12 | 18 ( 78.3) | 18 ( 90.0) | ||
| 13 | 0 ( 0.0) | 1 ( 5.0) | ||
| 123 | 1 ( 4.3) | 0 ( 0.0) | ||
| POR_maara1234 (%) | 0.020 | |||
| 2 | 1 ( 4.3) | 7 ( 35.0) | ||
| 3 | 17 ( 73.9) | 12 ( 60.0) | ||
| 4 | 5 ( 21.7) | 1 ( 5.0) | ||
| POR_tonkimat_6_mika (%) | 0.307 | |||
| 1 | 21 ( 91.3) | 15 ( 75.0) | ||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.0) | ||
| 4 | 1 ( 4.3) | 3 ( 15.0) | ||
| 5 | 0 ( 0.0) | 1 ( 5.0) | ||
| POR_lelu1234 (%) | 0.216 | |||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 2 ( 10.0) | ||
| 4 | 22 ( 95.7) | 17 ( 85.0) | ||
| 5 | 0 ( 0.0) | 1 ( 5.0) | ||
| POR_mat_vaiht = 2 (%) | 1 ( 4.3) | 0 ( 0.0) | 1.000 | |
| POR_maara123 (%) | 0.401 | |||
| 1 | 2 ( 8.7) | 0 ( 0.0) | ||
| 2 | 20 ( 87.0) | 19 ( 95.0) | ||
| 3 | 1 ( 4.3) | 1 ( 5.0) | ||
| POR_annostelu1234 (%) | 0.763 | |||
| 1 | 19 ( 82.6) | 18 ( 90.0) | ||
| 2 | 2 ( 8.7) | 1 ( 5.0) | ||
| 3 | 1 ( 4.3) | 1 ( 5.0) | ||
| 4 | 1 ( 4.3) | 0 ( 0.0) | ||
| Koulmax_1peru_2ops_3a_4amk_5yl (%) | 0.613 | |||
| 2 | 3 ( 13.0) | 2 ( 10.0) | ||
| 3 | 15 ( 65.2) | 13 ( 65.0) | ||
| 4 | 4 ( 17.4) | 2 ( 10.0) | ||
| 5 | 1 ( 4.3) | 3 ( 15.0) | ||
| Stressi_1erpal_4jnkv (%) | 0.846 | |||
| 1 | 4 ( 17.4) | 2 ( 10.0) | ||
| 2 | 4 ( 17.4) | 4 ( 20.0) | ||
| 3 | 8 ( 34.8) | 6 ( 30.0) | ||
| 4 | 7 ( 30.4) | 8 ( 40.0) | ||
| EMKUOLLJAKO = 1 (%) | 0 ( 0.0) | 20 (100.0) | <0.001 | |
| EMPOISJAKO = 1 (%) | 5 ( 21.7) | 14 ( 70.0) | 0.004 | |
| EMENKUOLLJAKO = 1 (%) | 1 ( 4.3) | 17 ( 85.0) | <0.001 | |
| EMENPOISJAKO = 1 (%) | 3 ( 13.0) | 13 ( 65.0) | 0.001 | |
| NIVEL_01 = 2 (%) | 8 ( 34.8) | 10 ( 50.0) | 0.485 | |
| MAKUU01 = 2 (%) | 7 ( 30.4) | 11 ( 55.0) | 0.187 | |
| KOKO_01 = 2 (%) | 7 ( 30.4) | 11 ( 55.0) | 0.187 | |
| OSA_01 = 2 (%) | 9 ( 39.1) | 9 ( 45.0) | 0.937 | |
| JOKUHYLK_01 = 2 (%) | 6 ( 26.1) | 12 ( 60.0) | 0.053 | |
| PLEUR_01 = 1 (%) | 4 ( 17.4) | 8 ( 40.0) | 0.191 | |
| PNEUM_01 = 2 (%) | 7 ( 30.4) | 11 ( 55.0) | 0.187 | |
| SAIRKARS_AST_TII = 1 (%) | 15 ( 65.2) | 18 ( 90.0) | 0.120 |
Yhteenveto poistojen mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table2 = KreateTableOne(x=tilapieni, factorVars=colnames(tilapienikat), strata='EMPOISJAKO')table2%>%
kable("html", align = "rrr", caption = "Data variable summary strat by EMPOIS") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 24 | 19 | ||
| Karjut_astsiem (mean (sd)) | 0.12 (0.45) | 0.79 (1.96) | 0.114 | |
| emakot (mean (sd)) | 295.88 (277.03) | 596.79 (518.67) | 0.019 | |
| ensikot (mean (sd)) | 37.08 (42.04) | 113.68 (176.56) | 0.046 | |
| lihasiat (mean (sd)) | 287.54 (535.14) | 500.21 (933.06) | 0.353 | |
| karjut (mean (sd)) | 2.25 (1.42) | 3.74 (1.91) | 0.006 | |
| kokemusave (mean (sd)) | 18.94 (9.43) | 16.37 (9.60) | 0.384 | |
| kokemusmax (mean (sd)) | 25.42 (10.78) | 25.26 (10.29) | 0.962 | |
| emakoitaper (mean (sd)) | 88.13 (43.78) | 123.52 (77.49) | 0.066 | |
| nivelpros (mean (sd)) | 2.37 (2.27) | 3.11 (2.85) | 0.349 | |
| paisepros (mean (sd)) | 6.74 (4.76) | 7.06 (3.61) | 0.809 | |
| keuhtulpros (mean (sd)) | 0.76 (0.73) | 1.31 (1.06) | 0.051 | |
| keuhkopros (mean (sd)) | 5.05 (8.11) | 10.88 (13.57) | 0.088 | |
| kokopros (mean (sd)) | 1.62 (1.80) | 2.04 (1.79) | 0.449 | |
| osapros (mean (sd)) | 11.72 (8.09) | 12.14 (5.50) | 0.849 | |
| emkuol (mean (sd)) | 7.42 (3.73) | 12.90 (6.90) | 0.002 | |
| empoisp (mean (sd)) | 41.22 (6.16) | 65.85 (17.65) | <0.001 | |
| Haastrooli_1OmEiosall_2OmOsall_3Esimies (%) | 0.311 | |||
| 1 | 4 ( 16.7) | 6 ( 31.6) | ||
| 2 | 18 ( 75.0) | 10 ( 52.6) | ||
| 3 | 2 ( 8.3) | 3 ( 15.8) | ||
| Tuotsuunta = 2 (%) | 13 ( 54.2) | 8 ( 42.1) | 0.632 | |
| Tautsu_012 (%) | 0.473 | |||
| 0 | 9 ( 37.5) | 4 ( 21.1) | ||
| 1 | 7 ( 29.2) | 6 ( 31.6) | ||
| 2 | 8 ( 33.3) | 9 ( 47.4) | ||
| PORSOSASTO_kertayt_0ei = 1 (%) | 8 ( 33.3) | 10 ( 52.6) | 0.336 | |
| PORS_pesu_0ei = 1 (%) | 20 ( 83.3) | 13 ( 68.4) | 0.432 | |
| PORS_desinf_0ei_1LIU_2KUIVA (%) | 0.894 | |||
| 0 | 4 ( 16.7) | 5 ( 26.3) | ||
| 1 | 11 ( 45.8) | 8 ( 42.1) | ||
| 2 | 6 ( 25.0) | 4 ( 21.1) | ||
| 12 | 3 ( 12.5) | 2 ( 10.5) | ||
| PORS_tyhjana_mi1vr_0ei = 1 (%) | 14 ( 58.3) | 12 ( 63.2) | 0.994 | |
| Tuhoelmerkkeja_0kylla_1ei = 1 (%) | 6 ( 25.0) | 4 ( 21.1) | 1.000 | |
| kissoja0on1ei (%) | 0.051 | |||
| 0 | 18 ( 75.0) | 8 ( 42.1) | ||
| 0.5 | 0 ( 0.0) | 2 ( 10.5) | ||
| 1 | 6 ( 25.0) | 9 ( 47.4) | ||
| Kotielain_sikalaan_0kylla_1ei = 1 (%) | 20 ( 83.3) | 14 ( 73.7) | 0.693 | |
| Vesi_1kunn_0oma = 1 (%) | 15 ( 62.5) | 12 ( 63.2) | 1.000 | |
| ClC = 1 (%) | 1 ( 4.2) | 2 ( 10.5) | 0.833 | |
| ClA = 0 (%) | 24 (100.0) | 19 (100.0) | NA | |
| SI = 1 (%) | 1 ( 4.2) | 3 ( 15.8) | 0.439 | |
| APP = 1 (%) | 4 ( 16.7) | 1 ( 5.3) | 0.497 | |
| Loisaika_1ennenpors_2_porskars = 2 (%) | 8 ( 33.3) | 8 ( 42.1) | 0.785 | |
| Ton_tiheys_1aina_2jaetaan = 2 (%) | 3 ( 12.5) | 1 ( 5.3) | 0.777 | |
| Muutelkaynn_0ei_1_satunn_2kaynnmuusaann (%) | 0.089 | |||
| 0 | 11 ( 45.8) | 5 ( 26.3) | ||
| 1 | 12 ( 50.0) | 9 ( 47.4) | ||
| 2 | 1 ( 4.2) | 5 ( 26.3) | ||
| maitokuume = 1 (%) | 12 ( 50.0) | 10 ( 52.6) | 1.000 | |
| metriitti = 1 (%) | 10 ( 41.7) | 9 ( 47.4) | 0.948 | |
| valuttelu = 1 (%) | 3 ( 12.5) | 2 ( 10.5) | 1.000 | |
| mastiitti = 1 (%) | 5 ( 20.8) | 5 ( 26.3) | 0.953 | |
| ontuma = 1 (%) | 15 ( 62.5) | 16 ( 84.2) | 0.217 | |
| syomattomyys = 1 (%) | 14 ( 58.3) | 8 ( 42.1) | 0.453 | |
| kuume = 1 (%) | 5 ( 20.8) | 1 ( 5.3) | 0.308 | |
| loukkaantuminen = 1 (%) | 11 ( 45.8) | 5 ( 26.3) | 0.319 | |
| AB_rutiinilaak = 1 (%) | 3 ( 12.5) | 3 ( 15.8) | 1.000 | |
| Oksitosiini_rutiinisti = 1 (%) | 7 ( 29.2) | 10 ( 52.6) | 0.212 | |
| Kaynnistys_rutiinisti = 1 (%) | 0 ( 0.0) | 4 ( 21.1) | 0.067 | |
| NSAID_porsituksessa_rutiini = 1 (%) | 6 ( 25.0) | 4 ( 21.1) | 1.000 | |
| OMATENSIKOT_0EI_1KYLLa = 1 (%) | 15 ( 62.5) | 13 ( 68.4) | 0.934 | |
| Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk (%) | 0.828 | |||
| 1 | 1 ( 4.2) | 0 ( 0.0) | ||
| 2 | 3 ( 12.5) | 2 ( 10.5) | ||
| 3 | 19 ( 79.2) | 16 ( 84.2) | ||
| 4 | 1 ( 4.2) | 1 ( 5.3) | ||
| Kiimantark_ryhmakaytt = 1 (%) | 21 ( 87.5) | 17 ( 89.5) | 1.000 | |
| Kiimantarkalkaa_vrkvier (%) | 0.224 | |||
| 0 | 5 ( 20.8) | 7 ( 36.8) | ||
| 1 | 16 ( 66.7) | 7 ( 36.8) | ||
| 3 | 2 ( 8.3) | 1 ( 5.3) | ||
| 4 | 0 ( 0.0) | 1 ( 5.3) | ||
| 5 | 1 ( 4.2) | 3 ( 15.8) | ||
| Kiimamerk_emakonselka = 1 (%) | 18 ( 75.0) | 19 (100.0) | 0.057 | |
| Kiimantark_postsiem = 1 (%) | 23 ( 95.8) | 18 ( 94.7) | 1.000 | |
| Postsiem_ryhmakaytt_havainnointi = 1 (%) | 21 ( 87.5) | 17 ( 89.5) | 1.000 | |
| Tiin_ultra2 (%) | 0.266 | |||
| 6 | 24 (100.0) | 17 ( 89.5) | ||
| 8 | 0 ( 0.0) | 1 ( 5.3) | ||
| 10 | 0 ( 0.0) | 1 ( 5.3) | ||
| Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen (%) | 0.085 | |||
| 0 | 10 ( 41.7) | 6 ( 31.6) | ||
| 1 | 0 ( 0.0) | 2 ( 10.5) | ||
| 2 | 2 ( 8.3) | 6 ( 31.6) | ||
| 3 | 2 ( 8.3) | 0 ( 0.0) | ||
| 4 | 10 ( 41.7) | 5 ( 26.3) | ||
| Pesantekomatmaara_1runsas_2jnkv_3niukka (%) | 0.269 | |||
| 1 | 3 ( 12.5) | 0 ( 0.0) | ||
| 2 | 17 ( 70.8) | 16 ( 84.2) | ||
| 3 | 4 ( 16.7) | 3 ( 15.8) | ||
| PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa = 2 (%) | 20 ( 83.3) | 15 ( 78.9) | 1.000 | |
| AS_maara123 (%) | 0.320 | |||
| 0 | 1 ( 4.2) | 0 ( 0.0) | ||
| 2 | 19 ( 79.2) | 18 ( 94.7) | ||
| 3 | 4 ( 16.7) | 1 ( 5.3) | ||
| AS_annostelu1234 (%) | 0.660 | |||
| 0 | 1 ( 4.2) | 1 ( 5.3) | ||
| 1 | 22 ( 91.7) | 18 ( 94.7) | ||
| 3 | 1 ( 4.2) | 0 ( 0.0) | ||
| AS_sairkars = 1 (%) | 6 ( 25.0) | 5 ( 26.3) | 1.000 | |
| AS_ruoklaite12345 = 4 (%) | 24 (100.0) | 17 ( 89.5) | 0.369 | |
| AS_ruokpaikka (%) | 0.266 | |||
| 0 | 0 ( 0.0) | 1 ( 5.3) | ||
| 1 | 24 (100.0) | 17 ( 89.5) | ||
| 4 | 0 ( 0.0) | 1 ( 5.3) | ||
| TII_alusta12345 = 1 (%) | 24 (100.0) | 19 (100.0) | NA | |
| TII_latt_rakenne1234 (%) | 0.471 | |||
| 1 | 4 ( 16.7) | 1 ( 5.3) | ||
| 12 | 2 ( 8.3) | 2 ( 10.5) | ||
| 13 | 18 ( 75.0) | 15 ( 78.9) | ||
| 23 | 0 ( 0.0) | 1 ( 5.3) | ||
| TII_kuiv_mat12345 (%) | 0.565 | |||
| 1 | 4 ( 16.7) | 1 ( 5.3) | ||
| 2 | 15 ( 62.5) | 16 ( 84.2) | ||
| 12 | 2 ( 8.3) | 1 ( 5.3) | ||
| 14 | 2 ( 8.3) | 1 ( 5.3) | ||
| 15 | 1 ( 4.2) | 0 ( 0.0) | ||
| TII_maara1234 (%) | 0.508 | |||
| 1 | 3 ( 12.5) | 1 ( 5.3) | ||
| 2 | 4 ( 16.7) | 1 ( 5.3) | ||
| 3 | 13 ( 54.2) | 12 ( 63.2) | ||
| 4 | 4 ( 16.7) | 4 ( 21.1) | ||
| 23 | 0 ( 0.0) | 1 ( 5.3) | ||
| TII_tonkimat_6_mika (%) | 0.186 | |||
| 1 | 23 ( 95.8) | 15 ( 78.9) | ||
| 2 | 1 ( 4.2) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.3) | ||
| 4 | 0 ( 0.0) | 1 ( 5.3) | ||
| 5 | 0 ( 0.0) | 2 ( 10.5) | ||
| TII_lelu1234 (%) | 0.296 | |||
| 2 | 2 ( 8.3) | 0 ( 0.0) | ||
| 4 | 21 ( 87.5) | 18 ( 94.7) | ||
| 5 | 1 ( 4.2) | 0 ( 0.0) | ||
| 24 | 0 ( 0.0) | 1 ( 5.3) | ||
| TII_maara123 (%) | 0.877 | |||
| 1 | 3 ( 12.5) | 1 ( 5.3) | ||
| 1.5 | 1 ( 4.2) | 1 ( 5.3) | ||
| 2 | 19 ( 79.2) | 16 ( 84.2) | ||
| 3 | 1 ( 4.2) | 1 ( 5.3) | ||
| TII_annostelu1234 (%) | 0.513 | |||
| 1 | 23 ( 95.8) | 17 ( 89.5) | ||
| 2 | 0 ( 0.0) | 1 ( 5.3) | ||
| 4 | 1 ( 4.2) | 1 ( 5.3) | ||
| TII_sairkars = 1 (%) | 23 ( 95.8) | 16 ( 84.2) | 0.439 | |
| POR_latt_rakenne1234 (%) | 0.164 | |||
| 1 | 2 ( 8.3) | 0 ( 0.0) | ||
| 2 | 3 ( 12.5) | 0 ( 0.0) | ||
| 12 | 18 ( 75.0) | 18 ( 94.7) | ||
| 13 | 0 ( 0.0) | 1 ( 5.3) | ||
| 123 | 1 ( 4.2) | 0 ( 0.0) | ||
| POR_maara1234 (%) | 0.265 | |||
| 2 | 5 ( 20.8) | 3 ( 15.8) | ||
| 3 | 14 ( 58.3) | 15 ( 78.9) | ||
| 4 | 5 ( 20.8) | 1 ( 5.3) | ||
| POR_tonkimat_6_mika (%) | 0.081 | |||
| 1 | 22 ( 91.7) | 14 ( 73.7) | ||
| 2 | 0 ( 0.0) | 1 ( 5.3) | ||
| 3 | 1 ( 4.2) | 0 ( 0.0) | ||
| 4 | 0 ( 0.0) | 4 ( 21.1) | ||
| 5 | 1 ( 4.2) | 0 ( 0.0) | ||
| POR_lelu1234 (%) | 0.643 | |||
| 2 | 1 ( 4.2) | 0 ( 0.0) | ||
| 3 | 1 ( 4.2) | 1 ( 5.3) | ||
| 4 | 21 ( 87.5) | 18 ( 94.7) | ||
| 5 | 1 ( 4.2) | 0 ( 0.0) | ||
| POR_mat_vaiht = 2 (%) | 0 ( 0.0) | 1 ( 5.3) | 0.906 | |
| POR_maara123 (%) | 0.433 | |||
| 1 | 2 ( 8.3) | 0 ( 0.0) | ||
| 2 | 21 ( 87.5) | 18 ( 94.7) | ||
| 3 | 1 ( 4.2) | 1 ( 5.3) | ||
| POR_annostelu1234 (%) | 0.386 | |||
| 1 | 20 ( 83.3) | 17 ( 89.5) | ||
| 2 | 2 ( 8.3) | 1 ( 5.3) | ||
| 3 | 2 ( 8.3) | 0 ( 0.0) | ||
| 4 | 0 ( 0.0) | 1 ( 5.3) | ||
| Koulmax_1peru_2ops_3a_4amk_5yl (%) | 0.833 | |||
| 2 | 2 ( 8.3) | 3 ( 15.8) | ||
| 3 | 16 ( 66.7) | 12 ( 63.2) | ||
| 4 | 4 ( 16.7) | 2 ( 10.5) | ||
| 5 | 2 ( 8.3) | 2 ( 10.5) | ||
| Stressi_1erpal_4jnkv (%) | 0.395 | |||
| 1 | 5 ( 20.8) | 1 ( 5.3) | ||
| 2 | 3 ( 12.5) | 5 ( 26.3) | ||
| 3 | 8 ( 33.3) | 6 ( 31.6) | ||
| 4 | 8 ( 33.3) | 7 ( 36.8) | ||
| EMKUOLLJAKO = 1 (%) | 6 ( 25.0) | 14 ( 73.7) | 0.004 | |
| EMPOISJAKO = 1 (%) | 0 ( 0.0) | 19 (100.0) | <0.001 | |
| EMENKUOLLJAKO = 1 (%) | 5 ( 20.8) | 13 ( 68.4) | 0.005 | |
| EMENPOISJAKO = 1 (%) | 1 ( 4.2) | 15 ( 78.9) | <0.001 | |
| NIVEL_01 = 2 (%) | 9 ( 37.5) | 9 ( 47.4) | 0.734 | |
| MAKUU01 = 2 (%) | 9 ( 37.5) | 9 ( 47.4) | 0.734 | |
| KOKO_01 = 2 (%) | 8 ( 33.3) | 10 ( 52.6) | 0.336 | |
| OSA_01 = 2 (%) | 10 ( 41.7) | 8 ( 42.1) | 1.000 | |
| JOKUHYLK_01 = 2 (%) | 8 ( 33.3) | 10 ( 52.6) | 0.336 | |
| PLEUR_01 = 1 (%) | 6 ( 25.0) | 6 ( 31.6) | 0.892 | |
| PNEUM_01 = 2 (%) | 8 ( 33.3) | 10 ( 52.6) | 0.336 | |
| SAIRKARS_AST_TII = 1 (%) | 18 ( 75.0) | 15 ( 78.9) | 1.000 |
Yhteenveto joku hylkays mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table3 = KreateTableOne(x=tilapieni, factorVars=colnames(tilapienikat), strata='JOKUHYLK_01')table3%>%
kable("html", align = "rrr", caption = "Data variable summary strat by JOKUHYLK") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 1 | 2 | p | test | |
|---|---|---|---|---|
| n | 25 | 18 | ||
| Karjut_astsiem (mean (sd)) | 0.36 (1.25) | 0.50 (1.54) | 0.745 | |
| emakot (mean (sd)) | 323.76 (312.17) | 574.78 (518.46) | 0.055 | |
| ensikot (mean (sd)) | 43.88 (48.42) | 108.50 (182.14) | 0.097 | |
| lihasiat (mean (sd)) | 256.44 (609.78) | 555.22 (870.39) | 0.192 | |
| karjut (mean (sd)) | 2.60 (1.53) | 3.33 (2.09) | 0.190 | |
| kokemusave (mean (sd)) | 19.30 (8.91) | 15.72 (10.11) | 0.227 | |
| kokemusmax (mean (sd)) | 26.80 (10.61) | 23.33 (10.14) | 0.288 | |
| emakoitaper (mean (sd)) | 88.71 (50.44) | 124.67 (73.11) | 0.063 | |
| nivelpros (mean (sd)) | 1.98 (2.24) | 3.69 (2.66) | 0.027 | |
| paisepros (mean (sd)) | 4.82 (2.72) | 9.75 (4.37) | <0.001 | |
| keuhtulpros (mean (sd)) | 0.77 (0.82) | 1.33 (0.97) | 0.046 | |
| keuhkopros (mean (sd)) | 1.60 (2.23) | 15.99 (13.07) | <0.001 | |
| kokopros (mean (sd)) | 0.95 (0.81) | 2.99 (2.10) | <0.001 | |
| osapros (mean (sd)) | 8.60 (4.88) | 16.51 (6.96) | <0.001 | |
| emkuol (mean (sd)) | 7.85 (4.73) | 12.60 (6.51) | 0.008 | |
| empoisp (mean (sd)) | 50.27 (19.07) | 54.66 (15.30) | 0.425 | |
| Haastrooli_1OmEiosall_2OmOsall_3Esimies (%) | 0.539 | |||
| 1 | 6 ( 24.0) | 4 ( 22.2) | ||
| 2 | 15 ( 60.0) | 13 ( 72.2) | ||
| 3 | 4 ( 16.0) | 1 ( 5.6) | ||
| Tuotsuunta = 2 (%) | 12 ( 48.0) | 9 ( 50.0) | 1.000 | |
| Tautsu_012 (%) | 0.922 | |||
| 0 | 8 ( 32.0) | 5 ( 27.8) | ||
| 1 | 7 ( 28.0) | 6 ( 33.3) | ||
| 2 | 10 ( 40.0) | 7 ( 38.9) | ||
| PORSOSASTO_kertayt_0ei = 1 (%) | 8 ( 32.0) | 10 ( 55.6) | 0.218 | |
| PORS_pesu_0ei = 1 (%) | 19 ( 76.0) | 14 ( 77.8) | 1.000 | |
| PORS_desinf_0ei_1LIU_2KUIVA (%) | 0.913 | |||
| 0 | 6 ( 24.0) | 3 ( 16.7) | ||
| 1 | 10 ( 40.0) | 9 ( 50.0) | ||
| 2 | 6 ( 24.0) | 4 ( 22.2) | ||
| 12 | 3 ( 12.0) | 2 ( 11.1) | ||
| PORS_tyhjana_mi1vr_0ei = 1 (%) | 14 ( 56.0) | 12 ( 66.7) | 0.697 | |
| Tuhoelmerkkeja_0kylla_1ei = 1 (%) | 7 ( 28.0) | 3 ( 16.7) | 0.616 | |
| kissoja0on1ei (%) | 0.962 | |||
| 0 | 15 ( 60.0) | 11 ( 61.1) | ||
| 0.5 | 1 ( 4.0) | 1 ( 5.6) | ||
| 1 | 9 ( 36.0) | 6 ( 33.3) | ||
| Kotielain_sikalaan_0kylla_1ei = 1 (%) | 21 ( 84.0) | 13 ( 72.2) | 0.578 | |
| Vesi_1kunn_0oma = 1 (%) | 18 ( 72.0) | 9 ( 50.0) | 0.249 | |
| ClC = 1 (%) | 1 ( 4.0) | 2 ( 11.1) | 0.767 | |
| ClA = 0 (%) | 25 (100.0) | 18 (100.0) | NA | |
| SI = 1 (%) | 0 ( 0.0) | 4 ( 22.2) | 0.052 | |
| APP = 1 (%) | 4 ( 16.0) | 1 ( 5.6) | 0.567 | |
| Loisaika_1ennenpors_2_porskars = 2 (%) | 10 ( 40.0) | 6 ( 33.3) | 0.899 | |
| Ton_tiheys_1aina_2jaetaan = 2 (%) | 3 ( 12.0) | 1 ( 5.6) | 0.853 | |
| Muutelkaynn_0ei_1_satunn_2kaynnmuusaann (%) | 0.082 | |||
| 0 | 10 ( 40.0) | 6 ( 33.3) | ||
| 1 | 14 ( 56.0) | 7 ( 38.9) | ||
| 2 | 1 ( 4.0) | 5 ( 27.8) | ||
| maitokuume = 1 (%) | 13 ( 52.0) | 9 ( 50.0) | 1.000 | |
| metriitti = 1 (%) | 9 ( 36.0) | 10 ( 55.6) | 0.336 | |
| valuttelu = 1 (%) | 1 ( 4.0) | 4 ( 22.2) | 0.175 | |
| mastiitti = 1 (%) | 6 ( 24.0) | 4 ( 22.2) | 1.000 | |
| ontuma = 1 (%) | 18 ( 72.0) | 13 ( 72.2) | 1.000 | |
| syomattomyys = 1 (%) | 12 ( 48.0) | 10 ( 55.6) | 0.857 | |
| kuume = 1 (%) | 2 ( 8.0) | 4 ( 22.2) | 0.378 | |
| loukkaantuminen = 1 (%) | 10 ( 40.0) | 6 ( 33.3) | 0.899 | |
| AB_rutiinilaak = 1 (%) | 2 ( 8.0) | 4 ( 22.2) | 0.378 | |
| Oksitosiini_rutiinisti = 1 (%) | 7 ( 28.0) | 10 ( 55.6) | 0.132 | |
| Kaynnistys_rutiinisti = 1 (%) | 0 ( 0.0) | 4 ( 22.2) | 0.052 | |
| NSAID_porsituksessa_rutiini = 1 (%) | 7 ( 28.0) | 3 ( 16.7) | 0.616 | |
| OMATENSIKOT_0EI_1KYLLa = 1 (%) | 19 ( 76.0) | 9 ( 50.0) | 0.150 | |
| Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk (%) | 0.415 | |||
| 1 | 1 ( 4.0) | 0 ( 0.0) | ||
| 2 | 2 ( 8.0) | 3 ( 16.7) | ||
| 3 | 20 ( 80.0) | 15 ( 83.3) | ||
| 4 | 2 ( 8.0) | 0 ( 0.0) | ||
| Kiimantark_ryhmakaytt = 1 (%) | 24 ( 96.0) | 14 ( 77.8) | 0.175 | |
| Kiimantarkalkaa_vrkvier (%) | 0.242 | |||
| 0 | 5 ( 20.0) | 7 ( 38.9) | ||
| 1 | 15 ( 60.0) | 8 ( 44.4) | ||
| 3 | 3 ( 12.0) | 0 ( 0.0) | ||
| 4 | 0 ( 0.0) | 1 ( 5.6) | ||
| 5 | 2 ( 8.0) | 2 ( 11.1) | ||
| Kiimamerk_emakonselka = 1 (%) | 21 ( 84.0) | 16 ( 88.9) | 0.992 | |
| Kiimantark_postsiem = 1 (%) | 24 ( 96.0) | 17 ( 94.4) | 1.000 | |
| Postsiem_ryhmakaytt_havainnointi = 1 (%) | 21 ( 84.0) | 17 ( 94.4) | 0.567 | |
| Tiin_ultra2 (%) | 0.348 | |||
| 6 | 24 ( 96.0) | 17 ( 94.4) | ||
| 8 | 1 ( 4.0) | 0 ( 0.0) | ||
| 10 | 0 ( 0.0) | 1 ( 5.6) | ||
| Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen (%) | 0.130 | |||
| 0 | 12 ( 48.0) | 4 ( 22.2) | ||
| 1 | 0 ( 0.0) | 2 ( 11.1) | ||
| 2 | 4 ( 16.0) | 4 ( 22.2) | ||
| 3 | 2 ( 8.0) | 0 ( 0.0) | ||
| 4 | 7 ( 28.0) | 8 ( 44.4) | ||
| Pesantekomatmaara_1runsas_2jnkv_3niukka (%) | 0.242 | |||
| 1 | 3 ( 12.0) | 0 ( 0.0) | ||
| 2 | 19 ( 76.0) | 14 ( 77.8) | ||
| 3 | 3 ( 12.0) | 4 ( 22.2) | ||
| PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa = 2 (%) | 20 ( 80.0) | 15 ( 83.3) | 1.000 | |
| AS_maara123 (%) | 0.376 | |||
| 0 | 1 ( 4.0) | 0 ( 0.0) | ||
| 2 | 20 ( 80.0) | 17 ( 94.4) | ||
| 3 | 4 ( 16.0) | 1 ( 5.6) | ||
| AS_annostelu1234 (%) | 0.472 | |||
| 0 | 1 ( 4.0) | 1 ( 5.6) | ||
| 1 | 24 ( 96.0) | 16 ( 88.9) | ||
| 3 | 0 ( 0.0) | 1 ( 5.6) | ||
| AS_sairkars = 1 (%) | 6 ( 24.0) | 5 ( 27.8) | 1.000 | |
| AS_ruoklaite12345 = 4 (%) | 24 ( 96.0) | 17 ( 94.4) | 1.000 | |
| AS_ruokpaikka (%) | 0.470 | |||
| 0 | 1 ( 4.0) | 0 ( 0.0) | ||
| 1 | 23 ( 92.0) | 18 (100.0) | ||
| 4 | 1 ( 4.0) | 0 ( 0.0) | ||
| TII_alusta12345 = 1 (%) | 25 (100.0) | 18 (100.0) | NA | |
| TII_latt_rakenne1234 (%) | 0.662 | |||
| 1 | 3 ( 12.0) | 2 ( 11.1) | ||
| 12 | 2 ( 8.0) | 2 ( 11.1) | ||
| 13 | 20 ( 80.0) | 13 ( 72.2) | ||
| 23 | 0 ( 0.0) | 1 ( 5.6) | ||
| TII_kuiv_mat12345 (%) | 0.181 | |||
| 1 | 3 ( 12.0) | 2 ( 11.1) | ||
| 2 | 15 ( 60.0) | 16 ( 88.9) | ||
| 12 | 3 ( 12.0) | 0 ( 0.0) | ||
| 14 | 3 ( 12.0) | 0 ( 0.0) | ||
| 15 | 1 ( 4.0) | 0 ( 0.0) | ||
| TII_maara1234 (%) | 0.064 | |||
| 1 | 4 ( 16.0) | 0 ( 0.0) | ||
| 2 | 4 ( 16.0) | 1 ( 5.6) | ||
| 3 | 15 ( 60.0) | 10 ( 55.6) | ||
| 4 | 2 ( 8.0) | 6 ( 33.3) | ||
| 23 | 0 ( 0.0) | 1 ( 5.6) | ||
| TII_tonkimat_6_mika (%) | 0.577 | |||
| 1 | 22 ( 88.0) | 16 ( 88.9) | ||
| 2 | 1 ( 4.0) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.6) | ||
| 4 | 1 ( 4.0) | 0 ( 0.0) | ||
| 5 | 1 ( 4.0) | 1 ( 5.6) | ||
| TII_lelu1234 (%) | 0.308 | |||
| 2 | 2 ( 8.0) | 0 ( 0.0) | ||
| 4 | 22 ( 88.0) | 17 ( 94.4) | ||
| 5 | 1 ( 4.0) | 0 ( 0.0) | ||
| 24 | 0 ( 0.0) | 1 ( 5.6) | ||
| TII_maara123 (%) | 0.037 | |||
| 1 | 4 ( 16.0) | 0 ( 0.0) | ||
| 1.5 | 0 ( 0.0) | 2 ( 11.1) | ||
| 2 | 21 ( 84.0) | 14 ( 77.8) | ||
| 3 | 0 ( 0.0) | 2 ( 11.1) | ||
| TII_annostelu1234 (%) | 0.313 | |||
| 1 | 22 ( 88.0) | 18 (100.0) | ||
| 2 | 1 ( 4.0) | 0 ( 0.0) | ||
| 4 | 2 ( 8.0) | 0 ( 0.0) | ||
| TII_sairkars = 1 (%) | 24 ( 96.0) | 15 ( 83.3) | 0.380 | |
| POR_latt_rakenne1234 (%) | 0.443 | |||
| 1 | 2 ( 8.0) | 0 ( 0.0) | ||
| 2 | 2 ( 8.0) | 1 ( 5.6) | ||
| 12 | 20 ( 80.0) | 16 ( 88.9) | ||
| 13 | 0 ( 0.0) | 1 ( 5.6) | ||
| 123 | 1 ( 4.0) | 0 ( 0.0) | ||
| POR_maara1234 (%) | 0.448 | |||
| 2 | 6 ( 24.0) | 2 ( 11.1) | ||
| 3 | 15 ( 60.0) | 14 ( 77.8) | ||
| 4 | 4 ( 16.0) | 2 ( 11.1) | ||
| POR_tonkimat_6_mika (%) | 0.668 | |||
| 1 | 20 ( 80.0) | 16 ( 88.9) | ||
| 2 | 1 ( 4.0) | 0 ( 0.0) | ||
| 3 | 1 ( 4.0) | 0 ( 0.0) | ||
| 4 | 2 ( 8.0) | 2 ( 11.1) | ||
| 5 | 1 ( 4.0) | 0 ( 0.0) | ||
| POR_lelu1234 (%) | 0.537 | |||
| 2 | 0 ( 0.0) | 1 ( 5.6) | ||
| 3 | 1 ( 4.0) | 1 ( 5.6) | ||
| 4 | 23 ( 92.0) | 16 ( 88.9) | ||
| 5 | 1 ( 4.0) | 0 ( 0.0) | ||
| POR_mat_vaiht = 2 (%) | 1 ( 4.0) | 0 ( 0.0) | 1.000 | |
| POR_maara123 (%) | 0.121 | |||
| 1 | 2 ( 8.0) | 0 ( 0.0) | ||
| 2 | 23 ( 92.0) | 16 ( 88.9) | ||
| 3 | 0 ( 0.0) | 2 ( 11.1) | ||
| POR_annostelu1234 (%) | 0.827 | |||
| 1 | 21 ( 84.0) | 16 ( 88.9) | ||
| 2 | 2 ( 8.0) | 1 ( 5.6) | ||
| 3 | 1 ( 4.0) | 1 ( 5.6) | ||
| 4 | 1 ( 4.0) | 0 ( 0.0) | ||
| Koulmax_1peru_2ops_3a_4amk_5yl (%) | 0.220 | |||
| 2 | 3 ( 12.0) | 2 ( 11.1) | ||
| 3 | 19 ( 76.0) | 9 ( 50.0) | ||
| 4 | 2 ( 8.0) | 4 ( 22.2) | ||
| 5 | 1 ( 4.0) | 3 ( 16.7) | ||
| Stressi_1erpal_4jnkv (%) | 0.527 | |||
| 1 | 4 ( 16.0) | 2 ( 11.1) | ||
| 2 | 4 ( 16.0) | 4 ( 22.2) | ||
| 3 | 10 ( 40.0) | 4 ( 22.2) | ||
| 4 | 7 ( 28.0) | 8 ( 44.4) | ||
| EMKUOLLJAKO = 1 (%) | 8 ( 32.0) | 12 ( 66.7) | 0.053 | |
| EMPOISJAKO = 1 (%) | 9 ( 36.0) | 10 ( 55.6) | 0.336 | |
| EMENKUOLLJAKO = 1 (%) | 7 ( 28.0) | 11 ( 61.1) | 0.063 | |
| EMENPOISJAKO = 1 (%) | 6 ( 24.0) | 10 ( 55.6) | 0.073 | |
| NIVEL_01 = 2 (%) | 5 ( 20.0) | 13 ( 72.2) | 0.002 | |
| MAKUU01 = 2 (%) | 4 ( 16.0) | 14 ( 77.8) | <0.001 | |
| KOKO_01 = 2 (%) | 4 ( 16.0) | 14 ( 77.8) | <0.001 | |
| OSA_01 = 2 (%) | 4 ( 16.0) | 14 ( 77.8) | <0.001 | |
| JOKUHYLK_01 = 2 (%) | 0 ( 0.0) | 18 (100.0) | <0.001 | |
| PLEUR_01 = 1 (%) | 1 ( 4.0) | 11 ( 61.1) | <0.001 | |
| PNEUM_01 = 2 (%) | 5 ( 20.0) | 13 ( 72.2) | 0.002 | |
| SAIRKARS_AST_TII = 1 (%) | 19 ( 76.0) | 14 ( 77.8) | 1.000 |
MCA
res_mca = MCA(tilapieni, quanti.sup = c(1:16), graph = FALSE) summary(res_mca)##
## Call:
## MCA(X = tilapieni, quanti.sup = c(1:16), graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6
## Variance 0.125 0.094 0.089 0.087 0.078 0.074
## % of var. 7.586 5.695 5.398 5.287 4.767 4.503
## Cumulative % of var. 7.586 13.282 18.680 23.967 28.734 33.238
## Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Dim.12
## Variance 0.065 0.064 0.061 0.057 0.056 0.053
## % of var. 3.932 3.871 3.680 3.446 3.411 3.230
## Cumulative % of var. 37.169 41.040 44.720 48.166 51.576 54.806
## Dim.13 Dim.14 Dim.15 Dim.16 Dim.17 Dim.18
## Variance 0.052 0.047 0.047 0.046 0.042 0.041
## % of var. 3.155 2.875 2.834 2.782 2.528 2.472
## Cumulative % of var. 57.961 60.836 63.670 66.452 68.980 71.453
## Dim.19 Dim.20 Dim.21 Dim.22 Dim.23 Dim.24
## Variance 0.039 0.036 0.034 0.032 0.030 0.030
## % of var. 2.394 2.212 2.048 1.930 1.848 1.807
## Cumulative % of var. 73.847 76.059 78.107 80.036 81.885 83.692
## Dim.25 Dim.26 Dim.27 Dim.28 Dim.29 Dim.30
## Variance 0.027 0.025 0.023 0.022 0.018 0.018
## % of var. 1.658 1.542 1.386 1.351 1.115 1.092
## Cumulative % of var. 85.350 86.891 88.278 89.629 90.743 91.836
## Dim.31 Dim.32 Dim.33 Dim.34 Dim.35 Dim.36
## Variance 0.017 0.016 0.016 0.014 0.012 0.012
## % of var. 1.044 0.979 0.953 0.880 0.759 0.735
## Cumulative % of var. 92.880 93.859 94.811 95.691 96.451 97.186
## Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 0.011 0.009 0.008 0.007 0.006 0.005
## % of var. 0.657 0.523 0.476 0.430 0.395 0.332
## Cumulative % of var. 97.844 98.367 98.842 99.273 99.668 100.000
##
## Individuals (the 10 first)
## Dim.1 ctr cos2 Dim.2
## 1 | 0.208 0.808 0.031 | -0.072
## 2 | -0.413 3.179 0.075 | -0.312
## 3 | -0.345 2.216 0.040 | 1.056
## 4 | 0.096 0.173 0.011 | -0.052
## 5 | 0.600 6.713 0.234 | -0.039
## 6 | -0.217 0.880 0.047 | 0.028
## 7 | 0.302 1.699 0.093 | 0.049
## 8 | -0.265 1.312 0.029 | 0.426
## 9 | -0.471 4.134 0.081 | -0.446
## 10 | -0.019 0.006 0.000 | 0.052
## ctr cos2 Dim.3 ctr
## 1 0.129 0.004 | 0.070 0.129
## 2 2.423 0.043 | 0.214 1.195
## 3 27.707 0.374 | 0.339 3.002
## 4 0.068 0.003 | 0.063 0.104
## 5 0.037 0.001 | -0.060 0.096
## 6 0.020 0.001 | -0.011 0.003
## 7 0.060 0.002 | 0.054 0.077
## 8 4.510 0.075 | -1.002 26.273
## 9 4.937 0.073 | 0.566 8.383
## 10 0.068 0.002 | -0.363 3.451
## cos2
## 1 0.004 |
## 2 0.020 |
## 3 0.038 |
## 4 0.005 |
## 5 0.002 |
## 6 0.000 |
## 7 0.003 |
## 8 0.415 |
## 9 0.117 |
## 10 0.099 |
##
## Categories (the 10 first)
## Dim.1 ctr cos2 v.test
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_1 | -0.229 0.129 0.016 -0.817 |
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_2 | 0.013 0.001 0.000 0.111 |
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_3 | 0.387 0.184 0.020 0.911 |
## Tuotsuunta_1 | 0.166 0.149 0.029 1.101 |
## Tuotsuunta_2 | -0.174 0.156 0.029 -1.101 |
## Tautsu_012_0 | -0.324 0.335 0.046 -1.383 |
## Tautsu_012_1 | 0.295 0.278 0.038 1.260 |
## Tautsu_012_2 | 0.022 0.002 0.000 0.115 |
## PORSOSASTO_kertayt_0ei_0 | -0.388 0.925 0.210 -2.967 |
## PORSOSASTO_kertayt_0ei_1 | 0.540 1.285 0.210 2.967 |
## Dim.2 ctr cos2 v.test
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_1 0.435 0.617 0.057 1.551 |
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_2 -0.171 0.269 0.055 -1.518 |
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_3 0.091 0.014 0.001 0.214 |
## Tuotsuunta_1 -0.121 0.105 0.015 -0.803 |
## Tuotsuunta_2 0.127 0.110 0.015 0.803 |
## Tautsu_012_0 0.068 0.019 0.002 0.289 |
## Tautsu_012_1 -0.190 0.153 0.016 -0.809 |
## Tautsu_012_2 0.093 0.048 0.006 0.488 |
## PORSOSASTO_kertayt_0ei_0 0.095 0.074 0.013 0.729 |
## PORSOSASTO_kertayt_0ei_1 -0.133 0.103 0.013 -0.729 |
## Dim.3 ctr cos2 v.test
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_1 0.569 1.115 0.098 2.029 |
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_2 -0.136 0.179 0.035 -1.206 |
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_3 -0.375 0.242 0.018 -0.881 |
## Tuotsuunta_1 -0.163 0.201 0.028 -1.080 |
## Tuotsuunta_2 0.171 0.211 0.028 1.080 |
## Tautsu_012_0 -0.253 0.288 0.028 -1.081 |
## Tautsu_012_1 -0.027 0.003 0.000 -0.116 |
## Tautsu_012_2 0.215 0.270 0.030 1.124 |
## PORSOSASTO_kertayt_0ei_0 -0.047 0.019 0.003 -0.359 |
## PORSOSASTO_kertayt_0ei_1 0.065 0.026 0.003 0.359 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## Haastrooli_1OmEiosall_2OmOsall_3Esimies | 0.030 0.064 0.104 |
## Tuotsuunta | 0.029 0.015 0.028 |
## Tautsu_012 | 0.058 0.016 0.038 |
## PORSOSASTO_kertayt_0ei | 0.210 0.013 0.003 |
## PORS_pesu_0ei | 0.004 0.001 0.085 |
## PORS_desinf_0ei_1LIU_2KUIVA | 0.027 0.019 0.205 |
## PORS_tyhjana_mi1vr_0ei | 0.000 0.245 0.003 |
## Tuhoelmerkkeja_0kylla_1ei | 0.036 0.072 0.020 |
## kissoja0on1ei | 0.111 0.059 0.068 |
## Kotielain_sikalaan_0kylla_1ei | 0.044 0.015 0.009 |
##
## Supplementary continuous variables (the 10 first)
## Dim.1 Dim.2 Dim.3
## Karjut_astsiem | 0.182 | 0.027 | -0.002 |
## emakot | 0.611 | 0.015 | -0.039 |
## ensikot | 0.427 | -0.011 | -0.039 |
## lihasiat | 0.139 | -0.004 | 0.040 |
## karjut | 0.463 | -0.012 | 0.104 |
## kokemusave | -0.270 | -0.138 | -0.144 |
## kokemusmax | -0.116 | -0.148 | -0.124 |
## emakoitaper | 0.509 | 0.033 | -0.059 |
## nivelpros | 0.228 | 0.074 | 0.065 |
## paisepros | 0.536 | 0.078 | 0.151 |To visualize the percentage of inertia explained by each MCA dimension:
eig.val <- res_mca$eig
barplot(eig.val[, 2],
names.arg = 1:nrow(eig.val),
main = "Variances Explained by Dimensions (%)",
xlab = "Principal Dimensions",
ylab = "Percentage of variances",
col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2],
type = "b", pch = 19, col = "red")
fviz_mca_var(res_mca, choice = "mca.cor",
repel = TRUE, # Avoid text overlapping (slow)
ggtheme = theme_minimal())
To visualize the percentage of inertia explained by each MCA dimension:
fviz_mca_var(res_mca, col.var = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # avoid text overlapping (slow)
ggtheme = theme_minimal()
)
Simple bar plots can also be used to visualize contribution of variable categories. The top 12 variable categories contributing to the first and second dimension:
# Contributions of rows to dimension 1
fviz_contrib(res_mca, choice = "var", axes = 1, top = 12)
# Contributions of rows to dimension 2
fviz_contrib(res_mca, choice = "var", axes = 2, top = 12)
Medication and diseases
# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="med.csv", header=TRUE)glimpse(med)## Observations: 43
## Variables: 36
## $ M_parasperyear <fctr> 2,4, 2,3, 2,4, 2,...
## $ M_parasot_1before_2inFAR_3noinfo_4allatonce <int> 1, 2, 2, 2, 1, 3, ...
## $ M_induction_0never_1sometimes <int> 1, 1, 1, 0, 1, 1, ...
## $ M_milkfever <int> 1, 0, 0, 1, 1, 1, ...
## $ M_metritis <int> 1, 0, 0, 1, 0, 1, ...
## $ M_secr <int> 1, 0, 0, 0, 0, 0, ...
## $ M_mastitis <int> 0, 0, 0, 1, 0, 0, ...
## $ M_lame <int> 1, 0, 1, 1, 1, 0, ...
## $ M_anorex <int> 1, 0, 0, 1, 1, 1, ...
## $ M_fever <int> 1, 0, 0, 0, 0, 0, ...
## $ M_injury <int> 1, 0, 0, 0, 0, 0, ...
## $ M_pregNSAIDS100_0_099_1 <int> 2, 0, 1, 1, 1, 0, ...
## $ M_pregAB100_0_099_1 <int> 2, 0, 1, 0, 1, 0, ...
## $ M_farNSAIDS100_05_630_31100 <int> 2, 0, 0, 0, 2, 0, ...
## $ M_farAB100_05_510_10 <int> 0, 2, 0, 0, 1, 0, ...
## $ M_routine_0no_1yes <int> 1, 1, 1, 1, 1, 1, ...
## $ M_routine_medic_NO <fctr> OX_FARNSAIDS, no,...
## $ M_rAB_NO <int> 0, 1, 0, 1, 0, 0, ...
## $ M_rOX <int> 1, 0, 0, 1, 1, 1, ...
## $ M_rIND_NO <int> 0, 0, 0, 0, 1, 0, ...
## $ M_rFARNSAIDS_NO <int> 1, 0, 1, 0, 0, 1, ...
## $ M_OX_10far_NUM_NO <fctr> 10, 3, 2, 2, 10, ...
## $ M_OX_obstex_preox <fctr> 1, 0, 0, 1, 0, 0,...
## $ M_OX_dosage_NO <fctr> 0,8, 0,5, 0,5, 0,...
## $ M_OX_between_NUM_NO <fctr> 0,5, 0,5, 2, 0,5,...
## $ M_OX_max_NUM_NO <fctr> 2,5, 4, 0,5, 3,5,...
## $ M_FAR_assist_NUM_NO <fctr> 50, 20, 5, 25, no...
## $ M_farNSAIDS_0no_1rout_2ifneed_NO <fctr> 1, 0, 1, 2, 2, 1,...
## $ M_lameness_NO <fctr> NSAIDS_PEN, 0, NS...
## $ M_AB_effectave_NUM_NO <fctr> 2,86, 4, 2,5, 3, ...
## $ OUT_SOW_mort_proNUM <int> 5, 5, 8, 27, 10, 0...
## $ OUT_SOW_mort_dic <int> 0, 0, 0, 1, 1, 0, ...
## $ OUT_SOW_totremproNUM <int> 34, 38, 53, 57, 65...
## $ OUT_SOW_totrem_dic <int> 0, 0, 1, 1, 1, 1, ...
## $ OUT_SOW_cullproNUM <int> 29, 33, 45, 30, 55...
## $ OUT_SOW_cull_dic <int> 0, 0, 1, 0, 1, 1, ...med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM ## [1] 29 33 45 30 55 64 30 31 24 28 29 36 70 27 42 22 40
## [18] 29 38 38 27 30 44 45 34 45 41 35 49 32 39 80 14 24
## [35] 35 56 39 50 42 35 33 107 42
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107colnames(med)=="OUT_SOW_cullproNUM"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE TRUE FALSEcolnames(med)=="OUT_SOWmortpro"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSEmed$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))
X<-medmca
for (i in 1:ncol(X)) {
levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}
colnames(X)[ apply(X, 2, anyNA) ]## character(0)X$M_induction_0never_1sometimes<-as.factor(X$M_induction_0never_1sometimes)
X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="purple") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+ scale_fill_manual("key")
library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")
library(tableone)
KreateTableOne = function(x, ...){
t1 = tableone::CreateTableOne(data=x, ...)
t2 = print(t1, quote=TRUE)
rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
return(t2)
}Yhteenveto kuolleisuuden mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')table1%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 23 | 20 | ||
| M_OX_10far_NUM_NO (mean (sd)) | 5.26 (3.11) | 5.05 (3.03) | 0.824 | |
| M_OX_between_NUM_NO (mean (sd)) | 3.96 (2.18) | 4.95 (2.80) | 0.199 | |
| M_OX_max_NUM_NO (mean (sd)) | 6.00 (2.37) | 6.55 (2.16) | 0.434 | |
| M_FAR_assist_NUM_NO (mean (sd)) | 3.96 (3.04) | 6.20 (3.32) | 0.026 | |
| M_AB_effectave_NUM_NO (mean (sd)) | 8.22 (3.16) | 7.95 (3.66) | 0.798 | |
| M_OX_dosage_NO (mean (sd)) | 5.26 (1.54) | 6.20 (2.02) | 0.092 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 4.74 (2.12) | 13.35 (3.27) | <0.001 | |
| OUT_SOW_totremproNUM (mean (sd)) | 8.91 (5.80) | 17.85 (8.03) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 11.78 (6.69) | 15.35 (8.20) | 0.124 | |
| M_parasperyear (%) | 0.322 | |||
| 0 | 0 ( 0.0) | 2 ( 10.0) | ||
| 1 | 1 ( 4.3) | 0 ( 0.0) | ||
| 2 | 2 ( 8.7) | 1 ( 5.0) | ||
| 2,1 | 1 ( 4.3) | 0 ( 0.0) | ||
| 2,2 | 0 ( 0.0) | 1 ( 5.0) | ||
| 2,3 | 8 (34.8) | 7 ( 35.0) | ||
| 2,4 | 11 (47.8) | 7 ( 35.0) | ||
| 4 | 0 ( 0.0) | 2 ( 10.0) | ||
| M_parasot_1before_2inFAR_3noinfo_4allatonce (%) | 0.743 | |||
| 1 | 10 (43.5) | 9 ( 45.0) | ||
| 2 | 9 (39.1) | 7 ( 35.0) | ||
| 3 | 4 (17.4) | 3 ( 15.0) | ||
| 4 | 0 ( 0.0) | 1 ( 5.0) | ||
| M_induction_0never_1sometimes (%) | 0.433 | |||
| 0 | 10 (43.5) | 6 ( 31.6) | ||
| 1 | 12 (52.2) | 13 ( 68.4) | ||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| M_milkfever = 1 (%) | 12 (52.2) | 10 ( 50.0) | 1.000 | |
| M_metritis = 1 (%) | 10 (43.5) | 9 ( 45.0) | 1.000 | |
| M_secr = 1 (%) | 2 ( 8.7) | 3 ( 15.0) | 0.868 | |
| M_mastitis = 1 (%) | 4 (17.4) | 6 ( 30.0) | 0.539 | |
| M_lame = 1 (%) | 15 (65.2) | 16 ( 80.0) | 0.461 | |
| M_anorex = 1 (%) | 10 (43.5) | 12 ( 60.0) | 0.438 | |
| M_fever = 1 (%) | 2 ( 8.7) | 4 ( 20.0) | 0.531 | |
| M_injury = 1 (%) | 10 (43.5) | 6 ( 30.0) | 0.551 | |
| M_pregNSAIDS100_0_099_1 (%) | 0.130 | |||
| 0 | 10 (45.5) | 5 ( 26.3) | ||
| 1 | 4 (18.2) | 9 ( 47.4) | ||
| 2 | 8 (36.4) | 5 ( 26.3) | ||
| M_pregAB100_0_099_1 (%) | 0.514 | |||
| 0 | 9 (40.9) | 5 ( 26.3) | ||
| 1 | 5 (22.7) | 7 ( 36.8) | ||
| 2 | 8 (36.4) | 7 ( 36.8) | ||
| M_farNSAIDS100_05_630_31100 (%) | 0.409 | |||
| 0 | 7 (31.8) | 7 ( 36.8) | ||
| 1 | 10 (45.5) | 5 ( 26.3) | ||
| 2 | 5 (22.7) | 7 ( 36.8) | ||
| M_farAB100_05_510_10 (%) | 0.273 | |||
| 0 | 12 (54.5) | 10 ( 52.6) | ||
| 1 | 8 (36.4) | 4 ( 21.1) | ||
| 2 | 2 ( 9.1) | 5 ( 26.3) | ||
| M_routine_0no_1yes = 1 (%) | 15 (65.2) | 13 ( 65.0) | 1.000 | |
| M_routine_medic_NO (%) | 0.265 | |||
| _COC | 1 ( 4.3) | 0 ( 0.0) | ||
| _FARNSAIDS | 3 (13.0) | 2 ( 10.0) | ||
| _FARNSAIDS_COC | 1 ( 4.3) | 0 ( 0.0) | ||
| _FARNSAIDS_PPAB | 0 ( 0.0) | 1 ( 5.0) | ||
| _PPAB | 1 ( 4.3) | 1 ( 5.0) | ||
| no | 9 (39.1) | 7 ( 35.0) | ||
| OX | 6 (26.1) | 3 ( 15.0) | ||
| OX_FARNSAIDS | 2 ( 8.7) | 0 ( 0.0) | ||
| OX_FARNSAIDS_COC_PPAB | 0 ( 0.0) | 1 ( 5.0) | ||
| OX_IND | 0 ( 0.0) | 4 ( 20.0) | ||
| OX_PPAB | 0 ( 0.0) | 1 ( 5.0) | ||
| M_rAB_NO = 1 (%) | 2 ( 8.7) | 4 ( 20.0) | 0.531 | |
| M_rOX = 1 (%) | 8 (34.8) | 9 ( 45.0) | 0.711 | |
| M_rIND_NO = 1 (%) | 0 ( 0.0) | 4 ( 20.0) | 0.084 | |
| M_rFARNSAIDS_NO = 1 (%) | 6 (26.1) | 4 ( 20.0) | 0.913 | |
| M_OX_obstex_preox (%) | 0.493 | |||
| 0 | 15 (65.2) | 11 ( 55.0) | ||
| 1 | 8 (34.8) | 8 ( 40.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 5.0) | ||
| M_farNSAIDS_0no_1rout_2ifneed_NO (%) | 0.667 | |||
| 0 | 1 ( 4.3) | 0 ( 0.0) | ||
| 1 | 6 (26.1) | 4 ( 20.0) | ||
| 2 | 15 (65.2) | 14 ( 70.0) | ||
| noinfo | 1 ( 4.3) | 2 ( 10.0) | ||
| M_lameness_NO (%) | 0.249 | |||
| _PEN | 0 ( 0.0) | 1 ( 5.0) | ||
| 0 | 9 (39.1) | 5 ( 25.0) | ||
| 3 | 0 ( 0.0) | 2 ( 10.0) | ||
| NSAIDS | 1 ( 4.3) | 1 ( 5.0) | ||
| NSAIDS_AMOX | 1 ( 4.3) | 0 ( 0.0) | ||
| NSAIDS_PEN | 10 (43.5) | 4 ( 20.0) | ||
| NSAIDS_PEN_AMOX | 0 ( 0.0) | 2 ( 10.0) | ||
| NSAIDS_PEN_SEL | 0 ( 0.0) | 1 ( 5.0) | ||
| NSAIDS_PEN_TRIM | 0 ( 0.0) | 1 ( 5.0) | ||
| NSAIDS_TETR | 0 ( 0.0) | 1 ( 5.0) | ||
| NSAIDS3 | 2 ( 8.7) | 2 ( 10.0) | ||
| OUT_SOW_mort_dic = 1 (%) | 0 ( 0.0) | 20 (100.0) | <0.001 | |
| OUT_SOW_totrem_dic = 1 (%) | 7 (30.4) | 15 ( 75.0) | 0.009 | |
| OUT_SOW_cull_dic = 1 (%) | 11 (47.8) | 10 ( 50.0) | 1.000 |
Yhteenveto poistojen mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')table2%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 22 | 21 | ||
| M_OX_10far_NUM_NO (mean (sd)) | 5.27 (3.10) | 5.05 (3.04) | 0.811 | |
| M_OX_between_NUM_NO (mean (sd)) | 4.18 (2.36) | 4.67 (2.69) | 0.533 | |
| M_OX_max_NUM_NO (mean (sd)) | 6.00 (2.12) | 6.52 (2.44) | 0.456 | |
| M_FAR_assist_NUM_NO (mean (sd)) | 5.00 (3.21) | 5.00 (3.54) | 1.000 | |
| M_AB_effectave_NUM_NO (mean (sd)) | 7.59 (3.49) | 8.62 (3.23) | 0.322 | |
| M_OX_dosage_NO (mean (sd)) | 5.45 (1.77) | 5.95 (1.88) | 0.376 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 8.73 (5.55) | 8.76 (4.73) | 0.983 | |
| OUT_SOW_totremproNUM (mean (sd)) | 7.77 (5.46) | 18.62 (6.82) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 7.18 (3.57) | 20.00 (4.27) | <0.001 | |
| M_parasperyear (%) | 0.267 | |||
| 0 | 2 ( 9.1) | 0 ( 0.0) | ||
| 1 | 1 ( 4.5) | 0 ( 0.0) | ||
| 2 | 1 ( 4.5) | 2 ( 9.5) | ||
| 2,1 | 1 ( 4.5) | 0 ( 0.0) | ||
| 2,2 | 1 ( 4.5) | 0 ( 0.0) | ||
| 2,3 | 9 (40.9) | 6 ( 28.6) | ||
| 2,4 | 7 (31.8) | 11 ( 52.4) | ||
| 4 | 0 ( 0.0) | 2 ( 9.5) | ||
| M_parasot_1before_2inFAR_3noinfo_4allatonce (%) | 0.760 | |||
| 1 | 9 (40.9) | 10 ( 47.6) | ||
| 2 | 8 (36.4) | 8 ( 38.1) | ||
| 3 | 4 (18.2) | 3 ( 14.3) | ||
| 4 | 1 ( 4.5) | 0 ( 0.0) | ||
| M_induction_0never_1sometimes (%) | 0.525 | |||
| 0 | 9 (42.9) | 7 ( 33.3) | ||
| 1 | 12 (57.1) | 13 ( 61.9) | ||
| 2 | 0 ( 0.0) | 1 ( 4.8) | ||
| M_milkfever = 1 (%) | 12 (54.5) | 10 ( 47.6) | 0.882 | |
| M_metritis = 1 (%) | 12 (54.5) | 7 ( 33.3) | 0.274 | |
| M_secr = 1 (%) | 2 ( 9.1) | 3 ( 14.3) | 0.956 | |
| M_mastitis = 1 (%) | 4 (18.2) | 6 ( 28.6) | 0.656 | |
| M_lame = 1 (%) | 16 (72.7) | 15 ( 71.4) | 1.000 | |
| M_anorex = 1 (%) | 13 (59.1) | 9 ( 42.9) | 0.448 | |
| M_fever = 1 (%) | 4 (18.2) | 2 ( 9.5) | 0.705 | |
| M_injury = 1 (%) | 8 (36.4) | 8 ( 38.1) | 1.000 | |
| M_pregNSAIDS100_0_099_1 (%) | 0.510 | |||
| 0 | 9 (42.9) | 6 ( 30.0) | ||
| 1 | 5 (23.8) | 8 ( 40.0) | ||
| 2 | 7 (33.3) | 6 ( 30.0) | ||
| M_pregAB100_0_099_1 (%) | 0.849 | |||
| 0 | 8 (38.1) | 6 ( 30.0) | ||
| 1 | 6 (28.6) | 6 ( 30.0) | ||
| 2 | 7 (33.3) | 8 ( 40.0) | ||
| M_farNSAIDS100_05_630_31100 (%) | 0.284 | |||
| 0 | 9 (42.9) | 5 ( 25.0) | ||
| 1 | 8 (38.1) | 7 ( 35.0) | ||
| 2 | 4 (19.0) | 8 ( 40.0) | ||
| M_farAB100_05_510_10 (%) | 0.049 | |||
| 0 | 15 (71.4) | 7 ( 35.0) | ||
| 1 | 3 (14.3) | 9 ( 45.0) | ||
| 2 | 3 (14.3) | 4 ( 20.0) | ||
| M_routine_0no_1yes = 1 (%) | 12 (54.5) | 16 ( 76.2) | 0.243 | |
| M_routine_medic_NO (%) | 0.222 | |||
| _COC | 0 ( 0.0) | 1 ( 4.8) | ||
| _FARNSAIDS | 4 (18.2) | 1 ( 4.8) | ||
| _FARNSAIDS_COC | 0 ( 0.0) | 1 ( 4.8) | ||
| _FARNSAIDS_PPAB | 0 ( 0.0) | 1 ( 4.8) | ||
| _PPAB | 0 ( 0.0) | 2 ( 9.5) | ||
| no | 11 (50.0) | 5 ( 23.8) | ||
| OX | 3 (13.6) | 6 ( 28.6) | ||
| OX_FARNSAIDS | 1 ( 4.5) | 1 ( 4.8) | ||
| OX_FARNSAIDS_COC_PPAB | 1 ( 4.5) | 0 ( 0.0) | ||
| OX_IND | 1 ( 4.5) | 3 ( 14.3) | ||
| OX_PPAB | 1 ( 4.5) | 0 ( 0.0) | ||
| M_rAB_NO = 1 (%) | 3 (13.6) | 3 ( 14.3) | 1.000 | |
| M_rOX = 1 (%) | 7 (31.8) | 10 ( 47.6) | 0.455 | |
| M_rIND_NO = 1 (%) | 1 ( 4.5) | 3 ( 14.3) | 0.566 | |
| M_rFARNSAIDS_NO = 1 (%) | 6 (27.3) | 4 ( 19.0) | 0.782 | |
| M_OX_obstex_preox (%) | 0.541 | |||
| 0 | 13 (59.1) | 13 ( 61.9) | ||
| 1 | 9 (40.9) | 7 ( 33.3) | ||
| noinfo | 0 ( 0.0) | 1 ( 4.8) | ||
| M_farNSAIDS_0no_1rout_2ifneed_NO (%) | 0.627 | |||
| 0 | 1 ( 4.5) | 0 ( 0.0) | ||
| 1 | 6 (27.3) | 4 ( 19.0) | ||
| 2 | 14 (63.6) | 15 ( 71.4) | ||
| noinfo | 1 ( 4.5) | 2 ( 9.5) | ||
| M_lameness_NO (%) | 0.700 | |||
| _PEN | 1 ( 4.5) | 0 ( 0.0) | ||
| 0 | 8 (36.4) | 6 ( 28.6) | ||
| 3 | 1 ( 4.5) | 1 ( 4.8) | ||
| NSAIDS | 1 ( 4.5) | 1 ( 4.8) | ||
| NSAIDS_AMOX | 0 ( 0.0) | 1 ( 4.8) | ||
| NSAIDS_PEN | 7 (31.8) | 7 ( 33.3) | ||
| NSAIDS_PEN_AMOX | 0 ( 0.0) | 2 ( 9.5) | ||
| NSAIDS_PEN_SEL | 1 ( 4.5) | 0 ( 0.0) | ||
| NSAIDS_PEN_TRIM | 1 ( 4.5) | 0 ( 0.0) | ||
| NSAIDS_TETR | 0 ( 0.0) | 1 ( 4.8) | ||
| NSAIDS3 | 2 ( 9.1) | 2 ( 9.5) | ||
| OUT_SOW_mort_dic = 1 (%) | 10 (45.5) | 10 ( 47.6) | 1.000 | |
| OUT_SOW_totrem_dic = 1 (%) | 5 (22.7) | 17 ( 81.0) | <0.001 | |
| OUT_SOW_cull_dic = 1 (%) | 0 ( 0.0) | 21 (100.0) | <0.001 |
Yhteenveto joku hylkays mukaan
res_mca = MCA(medmca, quanti.sup = c(20,21),quali.sup=c(17:19), graph = FALSE)summary(res_mca)##
## Call:
## MCA(X = medmca, quanti.sup = c(20, 21), quali.sup = c(17:19),
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6
## Variance 0.335 0.214 0.164 0.126 0.113 0.093
## % of var. 19.858 12.678 9.700 7.458 6.705 5.538
## Cumulative % of var. 19.858 32.536 42.236 49.693 56.398 61.937
## Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Dim.12
## Variance 0.086 0.079 0.071 0.065 0.056 0.047
## % of var. 5.076 4.708 4.193 3.841 3.298 2.799
## Cumulative % of var. 67.012 71.721 75.913 79.754 83.052 85.851
## Dim.13 Dim.14 Dim.15 Dim.16 Dim.17 Dim.18
## Variance 0.040 0.035 0.032 0.026 0.022 0.020
## % of var. 2.373 2.085 1.882 1.534 1.295 1.192
## Cumulative % of var. 88.224 90.309 92.192 93.725 95.020 96.212
## Dim.19 Dim.20 Dim.21 Dim.22 Dim.23 Dim.24
## Variance 0.017 0.014 0.012 0.009 0.007 0.004
## % of var. 1.034 0.830 0.694 0.533 0.440 0.257
## Cumulative % of var. 97.246 98.077 98.771 99.303 99.743 100.000
## Dim.25 Dim.26 Dim.27
## Variance 0.000 0.000 0.000
## % of var. 0.000 0.000 0.000
## Cumulative % of var. 100.000 100.000 100.000
##
## Individuals (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr
## 1 | -0.581 2.340 0.171 | 0.728 5.765
## 2 | 0.136 0.128 0.015 | -0.275 0.820
## 3 | -0.010 0.001 0.000 | -0.295 0.947
## 4 | -0.151 0.159 0.018 | -0.413 1.855
## 5 | -0.287 0.570 0.074 | 0.191 0.398
## 6 | -0.051 0.018 0.002 | -0.376 1.538
## 7 | 0.123 0.106 0.009 | 0.031 0.010
## 8 | -0.067 0.032 0.004 | -0.408 1.810
## 9 | 0.212 0.313 0.043 | -0.667 4.831
## 10 | -0.317 0.696 0.088 | 0.161 0.282
## cos2 Dim.3 ctr cos2
## 1 0.269 | 0.395 2.217 0.079 |
## 2 0.059 | 0.503 3.591 0.198 |
## 3 0.093 | -0.194 0.534 0.040 |
## 4 0.133 | -0.439 2.739 0.150 |
## 5 0.033 | -0.577 4.731 0.300 |
## 6 0.130 | 0.142 0.288 0.019 |
## 7 0.001 | 0.726 7.491 0.309 |
## 8 0.155 | -0.473 3.173 0.207 |
## 9 0.424 | 0.283 1.135 0.076 |
## 10 0.023 | -0.422 2.529 0.156 |
##
## Categories (the 10 first)
## Dim.1 ctr cos2 v.test Dim.2
## M_induction_0never_1sometimes.NA | 0.320 0.044 0.002 0.320 | -1.507
## M_induction_0never_1sometimes_0 | 0.564 2.210 0.189 2.815 | -0.362
## M_induction_0never_1sometimes_1 | -0.356 1.374 0.176 -2.718 | 0.255
## M_induction_0never_1sometimes_2 | -0.450 0.088 0.005 -0.450 | 0.922
## M_milkfever_0 | 0.013 0.002 0.000 0.084 | 0.009
## M_milkfever_1 | -0.013 0.002 0.000 -0.084 | -0.009
## M_metritis_0 | 0.259 0.699 0.085 1.888 | -0.029
## M_metritis_1 | -0.327 0.883 0.085 -1.888 | 0.036
## M_secr_0 | 0.105 0.180 0.083 1.869 | -0.216
## M_secr_1 | -0.795 1.371 0.083 -1.869 | 1.642
## ctr cos2 v.test Dim.3 ctr
## M_induction_0never_1sometimes.NA 1.542 0.054 -1.507 | 3.162 8.880
## M_induction_0never_1sometimes_0 1.422 0.078 -1.804 | -0.087 0.109
## M_induction_0never_1sometimes_1 1.103 0.090 1.946 | -0.048 0.052
## M_induction_0never_1sometimes_2 0.578 0.020 0.922 | -0.555 0.274
## M_milkfever_0 0.001 0.000 0.057 | 0.419 3.268
## M_milkfever_1 0.001 0.000 -0.057 | -0.400 3.119
## M_metritis_0 0.013 0.001 -0.208 | 0.057 0.068
## M_metritis_1 0.017 0.001 0.208 | -0.071 0.086
## M_secr_0 1.205 0.355 -3.860 | -0.140 0.661
## M_secr_1 9.158 0.355 3.860 | 1.064 5.025
## cos2 v.test
## M_induction_0never_1sometimes.NA 0.238 3.162 |
## M_induction_0never_1sometimes_0 0.005 -0.436 |
## M_induction_0never_1sometimes_1 0.003 -0.369 |
## M_induction_0never_1sometimes_2 0.007 -0.555 |
## M_milkfever_0 0.167 2.651 |
## M_milkfever_1 0.167 -2.651 |
## M_metritis_0 0.004 0.412 |
## M_metritis_1 0.004 -0.412 |
## M_secr_0 0.149 -2.501 |
## M_secr_1 0.149 2.501 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## M_induction_0never_1sometimes | 0.199 0.159 0.244 |
## M_milkfever | 0.000 0.000 0.167 |
## M_metritis | 0.085 0.001 0.004 |
## M_secr | 0.083 0.355 0.149 |
## M_mastitis | 0.010 0.006 0.118 |
## M_lame | 0.229 0.001 0.152 |
## M_anorex | 0.115 0.001 0.033 |
## M_fever | 0.011 0.015 0.417 |
## M_injury | 0.108 0.136 0.116 |
## M_pregNSAIDS100_0_099_1 | 0.906 0.538 0.416 |
##
## Supplementary categories
## Dim.1 cos2 v.test Dim.2 cos2
## OUT_SOW_mort_dic_0 | 0.026 0.001 0.180 | -0.077 0.007
## OUT_SOW_mort_dic_1 | -0.030 0.001 -0.180 | 0.088 0.007
## OUT_SOW_totrem_dic_0 | 0.013 0.000 0.084 | -0.036 0.001
## OUT_SOW_totrem_dic_1 | -0.013 0.000 -0.084 | 0.034 0.001
## OUT_SOW_cull_dic_0 | 0.029 0.001 0.192 | -0.216 0.049
## OUT_SOW_cull_dic_1 | -0.030 0.001 -0.192 | 0.226 0.049
## v.test Dim.3 cos2 v.test
## OUT_SOW_mort_dic_0 -0.533 | 0.041 0.002 0.283 |
## OUT_SOW_mort_dic_1 0.533 | -0.047 0.002 -0.283 |
## OUT_SOW_totrem_dic_0 -0.227 | 0.197 0.037 1.250 |
## OUT_SOW_totrem_dic_1 0.227 | -0.188 0.037 -1.250 |
## OUT_SOW_cull_dic_0 -1.429 | 0.146 0.022 0.967 |
## OUT_SOW_cull_dic_1 1.429 | -0.153 0.022 -0.967 |
##
## Supplementary categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## OUT_SOW_mort_dic | 0.001 0.007 0.002 |
## OUT_SOW_totrem_dic | 0.000 0.001 0.037 |
## OUT_SOW_cull_dic | 0.001 0.049 0.022 |
##
## Supplementary continuous variables
## Dim.1 Dim.2 Dim.3
## OUT_SOW_mort_proNUM | -0.105 | 0.140 | -0.129 |
## OUT_SOW_cullproNUM | -0.002 | 0.237 | -0.300 |To visualize the percentage of inertia explained by each MCA dimension:
eig.val <- res_mca$eig
barplot(eig.val[, 2],
names.arg = 1:nrow(eig.val),
main = "Variances Explained by Dimensions (%)",
xlab = "Principal Dimensions",
ylab = "Percentage of variances",
col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2],
type = "b", pch = 19, col = "red")
res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
var_sup = TRUE, var_lab_min_contrib = 0,
col_var = "Variable", symbol_var = "Type",
size_var = NULL, size_range = c(10, 300),
labels_size = 10, point_size = 56,
transitions = TRUE, labels_positions = NULL)res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
lab_var = NULL, , ind_lab_min_contrib = 0,
col_var = NULL, labels_size = 9,
point_opacity = 0.5, opacity_var = NULL, point_size = 64,
ellipses = FALSE, transitions = TRUE, labels_positions = NULL)fviz_mca_var(res_mca, choice = "quanti.sup",
ggtheme = theme_minimal())
## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```
##
## ```
## drawn <-
## c("13", "21", "24", "43", "40", "9", "16", "1", "34", "41")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
##
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
##
##
## The cluster 1 is made of individuals such as*. This group is characterized by1* and *1*. :
##
## - high frequency for the factors *M_farNSAIDS100_05_630_31100=M_farNSAIDS100_05_630_31100_2*, *M_pregNSAIDS100_0_099_1=M_pregNSAIDS100_0_099_1_2*, *M_pregAB100_0_099_1=M_pregAB100_0_099_1_2*, *M_routine_0no_1yes=M_routine_0no_1yes_1*, *M_injury=M_injury_1*, *M_rOX=M_rOX_1*, *M_farAB100_05_510_10=M_farAB100_05_510_10_1*, *M_secr=M_secr_1* and *M_induction_0never_1sometimes=M_induction_0never_1sometimes_1* (factors are sorted from the most common).
## - low frequency for the factors *M_farNSAIDS100_05_630_31100=M_farNSAIDS100_05_630_31100_0*, *M_pregAB100_0_099_1=M_pregAB100_0_099_1_0*, *M_pregNSAIDS100_0_099_1=M_pregNSAIDS100_0_099_1_0*, *M_routine_0no_1yes=M_routine_0no_1yes_0*, *M_injury=M_injury_0*, *M_rOX=M_rOX_0*, *M_farAB100_05_510_10=M_farAB100_05_510_10_0*, *M_induction_0never_1sometimes=M_induction_0never_1sometimes_0* and *M_secr=M_secr_0* (factors are sorted from the rarest).
##
## The cluster 2 is made of individuals such as*. This group is characterized by9* and *9*. :
##
## - high frequency for the factors *M_farNSAIDS100_05_630_31100=M_farNSAIDS100_05_630_31100_0*, *M_pregAB100_0_099_1=M_pregAB100_0_099_1_0*, *M_pregNSAIDS100_0_099_1=M_pregNSAIDS100_0_099_1_0*, *M_farAB100_05_510_10=M_farAB100_05_510_10_0*, *M_injury=M_injury_0*, *M_routine_0no_1yes=M_routine_0no_1yes_0*, *M_rOX=M_rOX_0* and *M_secr=M_secr_0* (factors are sorted from the most common).
## - low frequency for the factors *M_farNSAIDS100_05_630_31100=M_farNSAIDS100_05_630_31100_2*, *M_pregNSAIDS100_0_099_1=M_pregNSAIDS100_0_099_1_2*, *M_pregAB100_0_099_1=M_pregAB100_0_099_1_2*, *M_injury=M_injury_1*, *M_routine_0no_1yes=M_routine_0no_1yes_1*, *M_farAB100_05_510_10=M_farAB100_05_510_10_1*, *M_rOX=M_rOX_1* and *M_secr=M_secr_1* (factors are sorted from the rarest).
##
## The cluster 3 is made of individuals such as*. This group is characterized by13* and *13*. :
##
## - high frequency for the factors *M_farAB100_05_510_10=M_farAB100_05_510_10.NA*, *M_farNSAIDS100_05_630_31100=M_farNSAIDS100_05_630_31100.NA*, *M_pregAB100_0_099_1=M_pregAB100_0_099_1.NA*, *M_pregNSAIDS100_0_099_1=M_pregNSAIDS100_0_099_1.NA* and *M_OX_obstex_preox=M_OX_obstex_preox_noinfo* (factors are sorted from the most common).## **Results for the Hierarchical Clustering on Principal Components**
## name
## 1 "$data.clust"
## 2 "$desc.var"
## 3 "$desc.var$test.chi2"
## 4 "$desc.axes$category"
## 5 "$desc.axes"
## 6 "$desc.axes$quanti.var"
## 7 "$desc.axes$quanti"
## 8 "$desc.ind"
## 9 "$desc.ind$para"
## 10 "$desc.ind$dist"
## 11 "$call"
## 12 "$call$t"
## description
## 1 "dataset with the cluster of the individuals"
## 2 "description of the clusters by the variables"
## 3 "description of the cluster var. by the categorical var."
## 4 "description of the clusters by the categories."
## 5 "description of the clusters by the dimensions"
## 6 "description of the cluster var. by the axes"
## 7 "description of the clusters by the axes"
## 8 "description of the clusters by the individuals"
## 9 "parangons of each clusters"
## 10 "specific individuals"
## 11 "summary statistics"
## 12 "description of the tree"Biosecurity
# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="bio.csv", header=TRUE)glimpse(med)## Observations: 43
## Variables: 27
## $ B_Biosec <int> 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1,...
## $ B_Biosecused <int> 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,...
## $ B_Biosec_012 <int> 2, 0, 2, 2, 2, 2, 0, 0, 0, 1, 0, 1,...
## $ B_Pests <int> 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,...
## $ B_Entrancehuman <fctr> 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1...
## $ B_Entranceanimal <fctr> 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1...
## $ B_Handswash <int> 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0,...
## $ B_Bootswash <int> 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0,...
## $ B_Loadingbay <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ B_Entrancedriver <int> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0,...
## $ B_carcasstruckenter <int> 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1,...
## $ B_pestentercarcass <fctr> no, yes, yes, no, no, no, no, yes,...
## $ B_pestcontrol <fctr> catpois, catpois, catpoistrap, poi...
## $ B_pestsigns <fctr> yes, yes, yes, no, yes, yes, yes, ...
## $ B_birds <fctr> yes, yes, yes, no, no, no, no, no,...
## $ B_pestcontrolplan <fctr> yes, no, no, no, yes, no, no, no, ...
## $ B_cats <fctr> yes, yes, yes, no, no, yes, no, no...
## $ B_pets_in <fctr> yes, yes, no, no, no, yes, no, no,...
## $ B_biosecsumNUM_NO <int> 18, 14, 12, 16, 15, 12, 12, 8, 10, ...
## $ B_EXT_BIOSEC_SCORE_NUM_NO <dbl> 10.0, 8.0, 8.0, 8.0, 7.0, 8.0, 6.0,...
## $ B_INT_BIOSEC_SCOREB_NUM_NO <dbl> 8.0, 6.0, 1.0, 8.0, 6.0, 3.0, 5.0, ...
## $ OUT_SOW_mort_proNUM <int> 5, 5, 8, 27, 10, 0, 17, 13, 0, 6, 9...
## $ OUT_SOW_mort_dic <int> 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,...
## $ OUT_SOW_totremproNUM <int> 34, 38, 53, 57, 65, 64, 47, 44, 24,...
## $ OUT_SOW_totrem_dic <int> 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,...
## $ OUT_SOW_cullproNUM <int> 29, 33, 45, 30, 55, 64, 30, 31, 24,...
## $ OUT_SOW_cull_dic <int> 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0,...med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("B_pestcont")))#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="pink") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+ scale_fill_manual("key")
library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")
library(tableone)
KreateTableOne = function(x, ...){
t1 = tableone::CreateTableOne(data=x, ...)
t2 = print(t1, quote=TRUE)
rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
return(t2)
}Yhteenveto kuolleisuuden mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')table1%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 23 | 20 | ||
| B_biosecsumNUM_NO (mean (sd)) | 7.78 (3.52) | 8.35 (3.13) | 0.582 | |
| B_EXT_BIOSEC_SCORE_NUM_NO (mean (sd)) | 6.27 (2.66) | 6.45 (2.31) | 0.819 | |
| B_INT_BIOSEC_SCOREB_NUM_NO (mean (sd)) | 7.48 (4.40) | 9.30 (3.23) | 0.134 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 4.74 (2.12) | 13.35 (3.27) | <0.001 | |
| OUT_SOW_totremproNUM (mean (sd)) | 8.91 (5.80) | 17.85 (8.03) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 11.78 (6.69) | 15.35 (8.20) | 0.124 | |
| B_Biosec = 1 (%) | 15 (65.2) | 14 ( 70.0) | 0.994 | |
| B_Biosecused = 1 (%) | 9 (39.1) | 12 ( 60.0) | 0.289 | |
| B_Biosec_012 (%) | 0.731 | |||
| 0 | 8 (36.4) | 5 ( 26.3) | ||
| 1 | 6 (27.3) | 5 ( 26.3) | ||
| 2 | 8 (36.4) | 9 ( 47.4) | ||
| B_Pests = 1 (%) | 16 (69.6) | 19 ( 95.0) | 0.081 | |
| B_Entrancehuman (%) | 0.296 | |||
| 0 | 7 (30.4) | 9 ( 45.0) | ||
| 1 | 16 (69.6) | 10 ( 50.0) | ||
| n | 0 ( 0.0) | 1 ( 5.0) | ||
| B_Entranceanimal (%) | 0.251 | |||
| 0 | 9 (39.1) | 4 ( 20.0) | ||
| 1 | 14 (60.9) | 15 ( 75.0) | ||
| y | 0 ( 0.0) | 1 ( 5.0) | ||
| B_Handswash = 1 (%) | 14 (60.9) | 16 ( 80.0) | 0.303 | |
| B_Bootswash = 1 (%) | 14 (60.9) | 17 ( 85.0) | 0.156 | |
| B_Loadingbay = 1 (%) | 19 (82.6) | 17 ( 85.0) | 1.000 | |
| B_Entrancedriver = 1 (%) | 17 (73.9) | 13 ( 65.0) | 0.763 | |
| B_carcasstruckenter (%) | 0.435 | |||
| 0 | 7 (30.4) | 9 ( 45.0) | ||
| 1 | 15 (65.2) | 11 ( 55.0) | ||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| B_pestentercarcass (%) | 0.395 | |||
| 1 ( 4.3) | 0 ( 0.0) | |||
| no | 11 (47.8) | 13 ( 65.0) | ||
| yes | 11 (47.8) | 7 ( 35.0) | ||
| B_pestcontrol (%) | 0.158 | |||
| catdogpois | 0 ( 0.0) | 1 ( 5.0) | ||
| catdogpoistrap | 1 ( 4.3) | 0 ( 0.0) | ||
| catdogpoistrapfirm | 1 ( 4.3) | 0 ( 0.0) | ||
| catpois | 6 (26.1) | 4 ( 20.0) | ||
| catpoisother | 0 ( 0.0) | 1 ( 5.0) | ||
| catpoistrap | 5 (21.7) | 0 ( 0.0) | ||
| catpoistrapother | 1 ( 4.3) | 0 ( 0.0) | ||
| nothing | 0 ( 0.0) | 1 ( 5.0) | ||
| pois | 7 (30.4) | 6 ( 30.0) | ||
| poistrap | 2 ( 8.7) | 6 ( 30.0) | ||
| trap | 0 ( 0.0) | 1 ( 5.0) | ||
| B_pestsigns (%) | 0.428 | |||
| no | 5 (21.7) | 6 ( 30.0) | ||
| no0 | 0 ( 0.0) | 1 ( 5.0) | ||
| yes | 18 (78.3) | 13 ( 65.0) | ||
| B_birds (%) | 0.555 | |||
| no | 17 (73.9) | 14 ( 70.0) | ||
| no | 1 ( 4.3) | 0 ( 0.0) | ||
| yes | 5 (21.7) | 6 ( 30.0) | ||
| B_pestcontrolplan (%) | 0.168 | |||
| no | 19 (82.6) | 15 ( 75.0) | ||
| no | 2 ( 8.7) | 0 ( 0.0) | ||
| yes | 2 ( 8.7) | 5 ( 25.0) | ||
| B_cats = yes (%) | 19 (82.6) | 8 ( 40.0) | 0.010 | |
| B_pets_in = yes (%) | 6 (26.1) | 5 ( 25.0) | 1.000 | |
| OUT_SOW_mort_dic = 1 (%) | 0 ( 0.0) | 20 (100.0) | <0.001 | |
| OUT_SOW_totrem_dic = 1 (%) | 7 (30.4) | 15 ( 75.0) | 0.009 | |
| OUT_SOW_cull_dic = 1 (%) | 11 (47.8) | 10 ( 50.0) | 1.000 |
Yhteenveto poistojen mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')table2%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 22 | 21 | ||
| B_biosecsumNUM_NO (mean (sd)) | 8.50 (3.28) | 7.57 (3.37) | 0.365 | |
| B_EXT_BIOSEC_SCORE_NUM_NO (mean (sd)) | 6.71 (2.63) | 6.00 (2.30) | 0.355 | |
| B_INT_BIOSEC_SCOREB_NUM_NO (mean (sd)) | 9.18 (3.65) | 7.43 (4.17) | 0.149 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 8.73 (5.55) | 8.76 (4.73) | 0.983 | |
| OUT_SOW_totremproNUM (mean (sd)) | 7.77 (5.46) | 18.62 (6.82) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 7.18 (3.57) | 20.00 (4.27) | <0.001 | |
| B_Biosec = 1 (%) | 14 (63.6) | 15 ( 71.4) | 0.826 | |
| B_Biosecused = 1 (%) | 11 (50.0) | 10 ( 47.6) | 1.000 | |
| B_Biosec_012 (%) | 0.904 | |||
| 0 | 7 (33.3) | 6 ( 30.0) | ||
| 1 | 5 (23.8) | 6 ( 30.0) | ||
| 2 | 9 (42.9) | 8 ( 40.0) | ||
| B_Pests = 1 (%) | 17 (77.3) | 18 ( 85.7) | 0.750 | |
| B_Entrancehuman (%) | 0.398 | |||
| 0 | 7 (31.8) | 9 ( 42.9) | ||
| 1 | 15 (68.2) | 11 ( 52.4) | ||
| n | 0 ( 0.0) | 1 ( 4.8) | ||
| B_Entranceanimal (%) | 0.580 | |||
| 0 | 7 (31.8) | 6 ( 28.6) | ||
| 1 | 15 (68.2) | 14 ( 66.7) | ||
| y | 0 ( 0.0) | 1 ( 4.8) | ||
| B_Handswash = 1 (%) | 15 (68.2) | 15 ( 71.4) | 1.000 | |
| B_Bootswash = 1 (%) | 16 (72.7) | 15 ( 71.4) | 1.000 | |
| B_Loadingbay = 1 (%) | 16 (72.7) | 20 ( 95.2) | 0.113 | |
| B_Entrancedriver = 1 (%) | 17 (77.3) | 13 ( 61.9) | 0.444 | |
| B_carcasstruckenter (%) | 0.613 | |||
| 0 | 8 (36.4) | 8 ( 38.1) | ||
| 1 | 13 (59.1) | 13 ( 61.9) | ||
| 2 | 1 ( 4.5) | 0 ( 0.0) | ||
| B_pestentercarcass (%) | 0.186 | |||
| 0 ( 0.0) | 1 ( 4.8) | |||
| no | 15 (68.2) | 9 ( 42.9) | ||
| yes | 7 (31.8) | 11 ( 52.4) | ||
| B_pestcontrol (%) | 0.602 | |||
| catdogpois | 0 ( 0.0) | 1 ( 4.8) | ||
| catdogpoistrap | 0 ( 0.0) | 1 ( 4.8) | ||
| catdogpoistrapfirm | 0 ( 0.0) | 1 ( 4.8) | ||
| catpois | 6 (27.3) | 4 ( 19.0) | ||
| catpoisother | 1 ( 4.5) | 0 ( 0.0) | ||
| catpoistrap | 2 ( 9.1) | 3 ( 14.3) | ||
| catpoistrapother | 0 ( 0.0) | 1 ( 4.8) | ||
| nothing | 1 ( 4.5) | 0 ( 0.0) | ||
| pois | 8 (36.4) | 5 ( 23.8) | ||
| poistrap | 4 (18.2) | 4 ( 19.0) | ||
| trap | 0 ( 0.0) | 1 ( 4.8) | ||
| B_pestsigns (%) | 0.577 | |||
| no | 6 (27.3) | 5 ( 23.8) | ||
| no0 | 0 ( 0.0) | 1 ( 4.8) | ||
| yes | 16 (72.7) | 15 ( 71.4) | ||
| B_birds (%) | 0.577 | |||
| no | 16 (72.7) | 15 ( 71.4) | ||
| no | 0 ( 0.0) | 1 ( 4.8) | ||
| yes | 6 (27.3) | 5 ( 23.8) | ||
| B_pestcontrolplan (%) | 0.420 | |||
| no | 19 (86.4) | 15 ( 71.4) | ||
| no | 1 ( 4.5) | 1 ( 4.8) | ||
| yes | 2 ( 9.1) | 5 ( 23.8) | ||
| B_cats = yes (%) | 12 (54.5) | 15 ( 71.4) | 0.407 | |
| B_pets_in = yes (%) | 4 (18.2) | 7 ( 33.3) | 0.430 | |
| OUT_SOW_mort_dic = 1 (%) | 10 (45.5) | 10 ( 47.6) | 1.000 | |
| OUT_SOW_totrem_dic = 1 (%) | 5 (22.7) | 17 ( 81.0) | <0.001 | |
| OUT_SOW_cull_dic = 1 (%) | 0 ( 0.0) | 21 (100.0) | <0.001 |
Yhteenveto joku hylkays mukaan
res_mca = MCA(medmca, quanti.sup = c(20,21),quali.sup=c(17:19), graph = FALSE)summary(res_mca)##
## Call:
## MCA(X = medmca, quanti.sup = c(20, 21), quali.sup = c(17:19),
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6
## Variance 0.218 0.161 0.142 0.128 0.106 0.098
## % of var. 14.555 10.729 9.496 8.513 7.056 6.533
## Cumulative % of var. 14.555 25.284 34.780 43.292 50.348 56.881
## Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Dim.12
## Variance 0.095 0.082 0.073 0.065 0.056 0.051
## % of var. 6.351 5.434 4.892 4.312 3.737 3.413
## Cumulative % of var. 63.231 68.665 73.558 77.870 81.606 85.019
## Dim.13 Dim.14 Dim.15 Dim.16 Dim.17 Dim.18
## Variance 0.046 0.039 0.037 0.028 0.024 0.018
## % of var. 3.084 2.593 2.493 1.858 1.576 1.214
## Cumulative % of var. 88.103 90.696 93.189 95.046 96.623 97.837
## Dim.19 Dim.20 Dim.21 Dim.22 Dim.23 Dim.24
## Variance 0.014 0.012 0.004 0.002 0.000 0.000
## % of var. 0.954 0.830 0.254 0.125 0.000 0.000
## Cumulative % of var. 98.791 99.621 99.875 100.000 100.000 100.000
##
## Individuals (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | 0.145 0.224 0.021 | -0.221 0.706 0.049 |
## 2 | -0.484 2.498 0.178 | 0.404 2.361 0.124 |
## 3 | 0.246 0.643 0.051 | 0.127 0.232 0.013 |
## 4 | 0.232 0.574 0.054 | -0.344 1.711 0.118 |
## 5 | 0.256 0.696 0.066 | -0.347 1.738 0.121 |
## 6 | -0.125 0.167 0.011 | -0.071 0.073 0.004 |
## 7 | -0.333 1.182 0.116 | 0.184 0.490 0.035 |
## 8 | -0.146 0.226 0.015 | 0.310 1.386 0.066 |
## 9 | -0.275 0.805 0.066 | 0.220 0.702 0.042 |
## 10 | -0.233 0.578 0.056 | -0.227 0.747 0.053 |
## Dim.3 ctr cos2
## 1 -0.126 0.261 0.016 |
## 2 0.152 0.379 0.018 |
## 3 -0.706 8.144 0.418 |
## 4 0.136 0.301 0.018 |
## 5 -0.192 0.603 0.037 |
## 6 -0.387 2.449 0.110 |
## 7 0.195 0.620 0.040 |
## 8 -0.322 1.698 0.071 |
## 9 0.227 0.842 0.045 |
## 10 0.298 1.450 0.092 |
##
## Categories (the 10 first)
## Dim.1 ctr cos2 v.test Dim.2 ctr cos2
## B_Biosec_0 | -0.876 7.160 0.371 -3.947 | 0.747 7.056 0.269
## B_Biosec_1 | 0.423 3.457 0.371 3.947 | -0.361 3.406 0.269
## B_Biosecused_0 | -0.719 7.578 0.542 -4.771 | 0.351 2.449 0.129
## B_Biosecused_1 | 0.754 7.939 0.542 4.771 | -0.368 2.566 0.129
## B_Biosec_012.NA | 3.220 13.802 0.506 4.608 | 2.755 13.714 0.370
## B_Biosec_012_0 | -0.982 8.348 0.418 -4.190 | 0.870 8.896 0.328
## B_Biosec_012_1 | -0.218 0.348 0.016 -0.828 | -0.402 1.604 0.055
## B_Biosec_012_2 | 0.513 2.981 0.172 2.690 | -0.730 8.179 0.348
## B_Pests_0 | -0.515 1.411 0.061 -1.595 | 0.290 0.610 0.019
## B_Pests_1 | 0.118 0.322 0.061 1.595 | -0.066 0.139 0.019
## v.test Dim.3 ctr cos2 v.test
## B_Biosec_0 3.364 | 0.229 0.748 0.025 1.030 |
## B_Biosec_1 -3.364 | -0.110 0.361 0.025 -1.030 |
## B_Biosecused_0 2.329 | 0.206 0.948 0.044 1.363 |
## B_Biosecused_1 -2.329 | -0.215 0.994 0.044 -1.363 |
## B_Biosec_012.NA 3.944 | 0.172 0.060 0.001 0.246 |
## B_Biosec_012_0 3.714 | 0.219 0.634 0.021 0.933 |
## B_Biosec_012_1 -1.527 | 0.153 0.262 0.008 0.581 |
## B_Biosec_012_2 -3.825 | -0.286 1.422 0.054 -1.500 |
## B_Pests_0 0.900 | -0.110 0.099 0.003 -0.342 |
## B_Pests_1 -0.900 | 0.025 0.023 0.003 0.342 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## B_Biosec | 0.371 0.269 0.025 |
## B_Biosecused | 0.542 0.129 0.044 |
## B_Biosec_012 | 0.890 0.834 0.054 |
## B_Pests | 0.061 0.019 0.003 |
## B_Entrancehuman | 0.412 0.302 0.324 |
## B_Entranceanimal | 0.406 0.266 0.430 |
## B_Handswash | 0.011 0.101 0.305 |
## B_Bootswash | 0.000 0.077 0.409 |
## B_Loadingbay | 0.006 0.009 0.012 |
## B_Entrancedriver | 0.013 0.000 0.100 |
##
## Supplementary categories
## Dim.1 cos2 v.test Dim.2 cos2 v.test
## OUT_SOW_mort_dic_0 | -0.170 0.033 -1.182 | 0.154 0.027 1.071 |
## OUT_SOW_mort_dic_1 | 0.196 0.033 1.182 | -0.177 0.027 -1.071 |
## OUT_SOW_totrem_dic_0 | -0.095 0.009 -0.604 | 0.084 0.007 0.529 |
## OUT_SOW_totrem_dic_1 | 0.091 0.009 0.604 | -0.080 0.007 -0.529 |
## OUT_SOW_cull_dic_0 | 0.015 0.000 0.098 | -0.080 0.007 -0.532 |
## OUT_SOW_cull_dic_1 | -0.015 0.000 -0.098 | 0.084 0.007 0.532 |
## Dim.3 cos2 v.test
## OUT_SOW_mort_dic_0 -0.192 0.043 -1.336 |
## OUT_SOW_mort_dic_1 0.221 0.043 1.336 |
## OUT_SOW_totrem_dic_0 -0.071 0.005 -0.448 |
## OUT_SOW_totrem_dic_1 0.068 0.005 0.448 |
## OUT_SOW_cull_dic_0 -0.100 0.010 -0.663 |
## OUT_SOW_cull_dic_1 0.105 0.010 0.663 |
##
## Supplementary categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## OUT_SOW_mort_dic | 0.033 0.027 0.043 |
## OUT_SOW_totrem_dic | 0.009 0.007 0.005 |
## OUT_SOW_cull_dic | 0.000 0.007 0.010 |
##
## Supplementary continuous variables
## Dim.1 Dim.2 Dim.3
## OUT_SOW_mort_proNUM | 0.125 | -0.186 | 0.102 |
## OUT_SOW_cullproNUM | -0.026 | 0.056 | 0.190 |To visualize the percentage of inertia explained by each MCA dimension:
eig.val <- res_mca$eig
barplot(eig.val[, 2],
names.arg = 1:nrow(eig.val),
main = "Variances Explained by Dimensions (%)",
xlab = "Principal Dimensions",
ylab = "Percentage of variances",
col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2],
type = "b", pch = 19, col = "red")
res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
var_sup = TRUE, var_lab_min_contrib = 0,
col_var = "Variable", symbol_var = "Type",
size_var = NULL, size_range = c(10, 300),
labels_size = 10, point_size = 56,
transitions = TRUE, labels_positions = NULL)res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
lab_var = NULL, , ind_lab_min_contrib = 0,
col_var = NULL, labels_size = 9,
point_opacity = 0.5, opacity_var = NULL, point_size = 64,
ellipses = FALSE, transitions = TRUE, labels_positions = NULL)fviz_mca_var(res_mca, choice = "quanti.sup",
ggtheme = theme_minimal())
## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```
##
## ```
## drawn <-
## c("36", "21", "43", "16", "15", "30", "11", "19", "34", "2")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
##
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
##
##
## The cluster 1 is made of individuals such as*. This group is characterized by2* and *2*. :
##
## - high frequency for the factors *B_Biosecused=B_Biosecused_0*, *B_Biosec_012=B_Biosec_012_0*, *B_Biosec=B_Biosec_0* and *B_cats=B_cats_yes* (factors are sorted from the most common).
## - low frequency for the factors *B_Biosecused=B_Biosecused_1*, *B_Biosec_012=B_Biosec_012_2*, *B_Biosec=B_Biosec_1* and *B_cats=B_cats_no* (factors are sorted from the rarest).
##
## The cluster 2 is made of individuals such as*. This group is characterized by19* and *19*. :
##
## - high frequency for the factors *B_Biosec_012=B_Biosec_012_2*, *B_Biosecused=B_Biosecused_1*, *B_Biosec=B_Biosec_1* and *B_cats=B_cats_no* (factors are sorted from the most common).
## - low frequency for the factors *B_Biosecused=B_Biosecused_0*, *B_Biosec_012=B_Biosec_012_0*, *B_Biosec=B_Biosec_0* and *B_cats=B_cats_yes* (factors are sorted from the rarest).
##
## The cluster 3 is made of individuals such as*. This group is characterized by16* and *16*. :
##
## - high frequency for the factors *B_Biosec_012=B_Biosec_012.NA*, *B_carcasstruckenter=B_carcasstruckenter_2*, *B_Entranceanimal=B_Entranceanimal_y* and *B_Entrancehuman=B_Entrancehuman_n* (factors are sorted from the most common).## **Results for the Hierarchical Clustering on Principal Components**
## name
## 1 "$data.clust"
## 2 "$desc.var"
## 3 "$desc.var$test.chi2"
## 4 "$desc.axes$category"
## 5 "$desc.axes"
## 6 "$desc.axes$quanti.var"
## 7 "$desc.axes$quanti"
## 8 "$desc.ind"
## 9 "$desc.ind$para"
## 10 "$desc.ind$dist"
## 11 "$call"
## 12 "$call$t"
## description
## 1 "dataset with the cluster of the individuals"
## 2 "description of the clusters by the variables"
## 3 "description of the cluster var. by the categorical var."
## 4 "description of the clusters by the categories."
## 5 "description of the clusters by the dimensions"
## 6 "description of the cluster var. by the axes"
## 7 "description of the clusters by the axes"
## 8 "description of the clusters by the individuals"
## 9 "parangons of each clusters"
## 10 "specific individuals"
## 11 "summary statistics"
## 12 "description of the tree"Breeding management
# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="managbr.csv", header=TRUE)glimpse(med)## Observations: 43
## Variables: 35
## $ MG_BR_giltpurchage_NUM_NO <int> 4, 0, 0, 7, 3, 0, 0, 5, 7, 0, 0, ...
## $ MG_BR_giltchangebeforeins_NO <int> 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, ...
## $ MG_BR_giltflush_NO <fctr> 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1,...
## $ MG_BR_giltboarstart_NO <fctr> 7, 6, 7,5, 7,5, 7, 7,5, 7, 7, 7,...
## $ MG_BR_giltinsage_NO <fctr> 8, 7, 8, 7,5, 8, 9,5, 8, 8, 8, 8...
## $ MG_BR_heatgroup_NO <fctr> 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ MG_BR_heatdetec_startNUM_NO <fctr> 0, 0, 5, 0, 1, 0, 3, 3, 0, 1, 3,...
## $ MG_BR_heatmarkback_NO <fctr> 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,...
## $ MG_BR_artinspro_050_5099_100 <int> 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 1, ...
## $ MG_BR_farmsemenNUM_NO <int> 0, 0, 95, 0, 50, 0, 0, 0, 0, 0, 0...
## $ MG_BR_insonceNUM_NO <fctr> 0, 8, 0, 10, 0, 10, 80, 5, 0, 2,...
## $ MG_BR_once_012 <int> 0, 1, 0, 1, 0, 1, 2, 1, 0, 1, 0, ...
## $ MG_BR_instriple_NO <fctr> 1, 2, 10, 10, 15, 0, 0, 5, 0, 3,...
## $ MG_BR_triple_012 <fctr> 1, 1, 1, 1, 2, 0, 0, 1, 0, 1, 1,...
## $ MG_BR_nopregus <fctr> 1, 1, 2, 1, 2, 0, 0, 1, 1, 1, 0,...
## $ MG_BR_bedtype_NO <int> 0, 1, 12, 0, 0, 14, 1, 1, 0, 0, 1...
## $ MG_BR_bedny <int> 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, ...
## $ MG_BR_amount <int> 4, 2, 1, 0, 0, 1, 3, 2, 4, 4, 2, ...
## $ MG_BR_rootny <int> 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, ...
## $ MG_BR_toyny <fctr> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 4,...
## $ MG_BR_dirt_NUM_NO <int> 30, 0, 0, 20, 40, 0, 20, 30, 10, ...
## $ MG_BR_animdirtmed <int> 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, ...
## $ MG_BR_feedtype <int> 4, 1, 25, 4, 4, 4, 4, 4, 4, 4, 4,...
## $ MG_BR_feedclean <fctr> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,...
## $ MG_BR_calm <int> 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, ...
## $ MG_BR_dirtanim_NUM_NO <int> 20, 0, 10, 10, NA, 20, 30, 30, 10...
## $ MG_BR_dirtanimmed <int> 2, 1, 1, 1, NA, 2, 2, 2, 1, NA, 2...
## $ MG_BR_ster <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ MG_BR_sowsperboar_NUM_NO <fctr> 150, 37, 75, 92, 525, 30, 115, 2...
## $ OUT_SOW_mort_proNUM <int> 5, 5, 8, 27, 10, 0, 17, 13, 0, 6,...
## $ OUT_SOW_mort_dic <int> 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, ...
## $ OUT_SOW_totremproNUM <int> 34, 38, 53, 57, 65, 64, 47, 44, 2...
## $ OUT_SOW_totrem_dic <int> 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, ...
## $ OUT_SOW_cullproNUM <int> 29, 33, 45, 30, 55, 64, 30, 31, 2...
## $ OUT_SOW_cull_dic <int> 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, ...med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM ## [1] 29 33 45 30 55 64 30 31 24 28 29 36 70 27 42 22 40
## [18] 29 38 38 27 30 44 45 34 45 41 35 49 32 39 80 14 24
## [35] 35 56 39 50 42 35 33 107 42
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107colnames(med)=="OUT_SOW_cullproNUM"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] TRUE FALSEcolnames(med)=="OUT_SOWmortpro"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSEmed$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))
X<-medmca
colnames(X)[ apply(X, 2, anyNA) ]## [1] "MG_BR_once_012" "MG_BR_animdirtmed" "MG_BR_dirtanimmed"for (i in 1:ncol(X)) {
levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}
X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)
medmca<-X #colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="yellow") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+ scale_fill_manual("key")
library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")
library(tableone)
KreateTableOne = function(x, ...){
t1 = tableone::CreateTableOne(data=x, ...)
t2 = print(t1, quote=TRUE)
rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
return(t2)
}Yhteenveto kuolleisuuden mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')table1%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 23 | 20 | ||
| MG_BR_giltpurchage_NUM_NO (mean (sd)) | 3.00 (2.63) | 2.40 (2.26) | 0.430 | |
| MG_BR_heatdetec_startNUM_NO (mean (sd)) | 3.35 (2.44) | 3.55 (2.37) | 0.785 | |
| MG_BR_farmsemenNUM_NO (mean (sd)) | 1.96 (2.06) | 2.00 (1.97) | 0.944 | |
| MG_BR_insonceNUM_NO (mean (sd)) | 4.04 (3.75) | 6.15 (3.99) | 0.082 | |
| MG_BR_dirt_NUM_NO (mean (sd)) | 2.27 (1.52) | 2.59 (1.54) | 0.527 | |
| MG_BR_dirtanim_NUM_NO (mean (sd)) | 2.52 (1.54) | 2.79 (1.23) | 0.552 | |
| MG_BR_sowsperboar_NUM_NO (mean (sd)) | 19.65 (10.08) | 17.55 (11.15) | 0.520 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 4.74 (2.12) | 13.35 (3.27) | <0.001 | |
| OUT_SOW_totremproNUM (mean (sd)) | 8.91 (5.80) | 17.85 (8.03) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 11.78 (6.69) | 15.35 (8.20) | 0.124 | |
| MG_BR_giltchangebeforeins_NO = 1 (%) | 9 (39.1) | 10 ( 50.0) | 0.683 | |
| MG_BR_giltflush_NO (%) | 0.299 | |||
| 0 | 13 (56.5) | 10 ( 50.0) | ||
| 1 | 8 (34.8) | 10 ( 50.0) | ||
| noneed | 2 ( 8.7) | 0 ( 0.0) | ||
| MG_BR_giltboarstart_NO (%) | 0.158 | |||
| 0 | 4 (17.4) | 0 ( 0.0) | ||
| 3 | 1 ( 4.3) | 0 ( 0.0) | ||
| 4 | 0 ( 0.0) | 1 ( 5.0) | ||
| 6 | 4 (17.4) | 4 ( 20.0) | ||
| 6,5 | 3 (13.0) | 0 ( 0.0) | ||
| 7 | 6 (26.1) | 10 ( 50.0) | ||
| 7,5 | 4 (17.4) | 4 ( 20.0) | ||
| 8 | 1 ( 4.3) | 0 ( 0.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_BR_giltinsage_NO (%) | 0.139 | |||
| 0 | 3 (13.0) | 0 ( 0.0) | ||
| 2ndheat | 0 ( 0.0) | 1 ( 5.0) | ||
| 6 | 0 ( 0.0) | 1 ( 5.0) | ||
| 7 | 2 ( 8.7) | 0 ( 0.0) | ||
| 7,5 | 1 ( 4.3) | 1 ( 5.0) | ||
| 8 | 14 (60.9) | 11 ( 55.0) | ||
| 8,5 | 0 ( 0.0) | 4 ( 20.0) | ||
| 9 | 1 ( 4.3) | 0 ( 0.0) | ||
| 9,5 | 2 ( 8.7) | 1 ( 5.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_BR_heatgroup_NO (%) | 0.361 | |||
| 0 | 5 (21.7) | 4 ( 20.0) | ||
| 1 | 17 (73.9) | 14 ( 70.0) | ||
| no | 1 ( 4.3) | 0 ( 0.0) | ||
| noinfo | 0 ( 0.0) | 2 ( 10.0) | ||
| MG_BR_heatmarkback_NO (%) | 0.211 | |||
| 0 | 8 (34.8) | 3 ( 15.0) | ||
| 1 | 15 (65.2) | 16 ( 80.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_BR_artinspro_050_5099_100 (%) | 0.785 | |||
| 0 | 1 ( 4.3) | 1 ( 5.0) | ||
| 1 | 8 (34.8) | 5 ( 25.0) | ||
| 2 | 14 (60.9) | 14 ( 70.0) | ||
| MG_BR_once_012 (%) | 0.059 | |||
| 0 | 10 (45.5) | 5 ( 26.3) | ||
| 1 | 12 (54.5) | 10 ( 52.6) | ||
| 2 | 0 ( 0.0) | 4 ( 21.1) | ||
| MG_BR_instriple_NO (%) | 0.630 | |||
| 0 | 7 (30.4) | 4 ( 20.0) | ||
| 1 | 1 ( 4.3) | 2 ( 10.0) | ||
| 10 | 5 (21.7) | 6 ( 30.0) | ||
| 15 | 0 ( 0.0) | 2 ( 10.0) | ||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| 3 | 1 ( 4.3) | 1 ( 5.0) | ||
| 30 | 1 ( 4.3) | 0 ( 0.0) | ||
| 33 | 0 ( 0.0) | 1 ( 5.0) | ||
| 5 | 6 (26.1) | 3 ( 15.0) | ||
| noinfo | 1 ( 4.3) | 1 ( 5.0) | ||
| MG_BR_triple_012 (%) | 0.621 | |||
| 0 | 7 (30.4) | 4 ( 20.0) | ||
| 1 | 14 (60.9) | 12 ( 60.0) | ||
| 2 | 1 ( 4.3) | 3 ( 15.0) | ||
| noinfo | 1 ( 4.3) | 1 ( 5.0) | ||
| MG_BR_nopregus (%) | 0.179 | |||
| 0 | 10 (43.5) | 3 ( 15.0) | ||
| 1 | 10 (43.5) | 12 ( 60.0) | ||
| 2 | 3 (13.0) | 4 ( 20.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_BR_bedtype_NO (%) | 0.229 | |||
| 0 | 7 (30.4) | 11 ( 55.0) | ||
| 1 | 7 (30.4) | 4 ( 20.0) | ||
| 2 | 1 ( 4.3) | 4 ( 20.0) | ||
| 5 | 1 ( 4.3) | 0 ( 0.0) | ||
| 12 | 3 (13.0) | 1 ( 5.0) | ||
| 14 | 2 ( 8.7) | 0 ( 0.0) | ||
| 25 | 1 ( 4.3) | 0 ( 0.0) | ||
| 125 | 1 ( 4.3) | 0 ( 0.0) | ||
| MG_BR_bedny = 1 (%) | 16 (69.6) | 9 ( 45.0) | 0.187 | |
| MG_BR_amount (%) | 0.393 | |||
| 0 | 2 ( 8.7) | 5 ( 25.0) | ||
| 1 | 5 (21.7) | 1 ( 5.0) | ||
| 2 | 3 (13.0) | 3 ( 15.0) | ||
| 3 | 7 (30.4) | 5 ( 25.0) | ||
| 4 | 6 (26.1) | 6 ( 30.0) | ||
| MG_BR_rootny = 1 (%) | 17 (73.9) | 8 ( 40.0) | 0.053 | |
| MG_BR_toyny (%) | 0.090 | |||
| 0 | 16 (69.6) | 8 ( 40.0) | ||
| 1 | 6 (26.1) | 10 ( 50.0) | ||
| 4 | 1 ( 4.3) | 0 ( 0.0) | ||
| y | 0 ( 0.0) | 2 ( 10.0) | ||
| MG_BR_animdirtmed = 2 (%) | 8 (36.4) | 9 ( 52.9) | 0.478 | |
| MG_BR_feedtype (%) | 0.145 | |||
| 1 | 1 ( 4.3) | 0 ( 0.0) | ||
| 2 | 0 ( 0.0) | 2 ( 10.0) | ||
| 3 | 3 (13.0) | 0 ( 0.0) | ||
| 4 | 18 (78.3) | 18 ( 90.0) | ||
| 25 | 1 ( 4.3) | 0 ( 0.0) | ||
| MG_BR_feedclean (%) | 0.246 | |||
| 0 | 20 (87.0) | 15 ( 75.0) | ||
| 1 | 2 ( 8.7) | 5 ( 25.0) | ||
| no | 1 ( 4.3) | 0 ( 0.0) | ||
| MG_BR_calm (%) | 0.506 | |||
| 0 | 1 ( 4.3) | 0 ( 0.0) | ||
| 1 | 21 (91.3) | 18 ( 90.0) | ||
| 2 | 1 ( 4.3) | 2 ( 10.0) | ||
| MG_BR_dirtanimmed = 2 (%) | 9 (42.9) | 10 ( 52.6) | 0.763 | |
| MG_BR_ster = 1 (%) | 4 (17.4) | 2 ( 10.0) | 0.798 | |
| OUT_SOW_mort_dic = 1 (%) | 0 ( 0.0) | 20 (100.0) | <0.001 | |
| OUT_SOW_totrem_dic = 1 (%) | 7 (30.4) | 15 ( 75.0) | 0.009 | |
| OUT_SOW_cull_dic = 1 (%) | 11 (47.8) | 10 ( 50.0) | 1.000 |
Yhteenveto poistojen mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')table2%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 22 | 21 | ||
| MG_BR_giltpurchage_NUM_NO (mean (sd)) | 3.45 (2.74) | 1.95 (1.88) | 0.043 | |
| MG_BR_heatdetec_startNUM_NO (mean (sd)) | 2.91 (2.20) | 4.00 (2.49) | 0.135 | |
| MG_BR_farmsemenNUM_NO (mean (sd)) | 1.36 (1.05) | 2.62 (2.52) | 0.037 | |
| MG_BR_insonceNUM_NO (mean (sd)) | 5.86 (4.16) | 4.14 (3.64) | 0.157 | |
| MG_BR_dirt_NUM_NO (mean (sd)) | 2.26 (1.19) | 2.55 (1.79) | 0.562 | |
| MG_BR_dirtanim_NUM_NO (mean (sd)) | 2.80 (1.28) | 2.50 (1.50) | 0.501 | |
| MG_BR_sowsperboar_NUM_NO (mean (sd)) | 18.41 (10.39) | 18.95 (10.89) | 0.868 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 8.73 (5.55) | 8.76 (4.73) | 0.983 | |
| OUT_SOW_totremproNUM (mean (sd)) | 7.77 (5.46) | 18.62 (6.82) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 7.18 (3.57) | 20.00 (4.27) | <0.001 | |
| MG_BR_giltchangebeforeins_NO = 1 (%) | 8 (36.4) | 11 ( 52.4) | 0.453 | |
| MG_BR_giltflush_NO (%) | 0.128 | |||
| 0 | 15 (68.2) | 8 ( 38.1) | ||
| 1 | 6 (27.3) | 12 ( 57.1) | ||
| noneed | 1 ( 4.5) | 1 ( 4.8) | ||
| MG_BR_giltboarstart_NO (%) | 0.530 | |||
| 0 | 2 ( 9.1) | 2 ( 9.5) | ||
| 3 | 1 ( 4.5) | 0 ( 0.0) | ||
| 4 | 1 ( 4.5) | 0 ( 0.0) | ||
| 6 | 5 (22.7) | 3 ( 14.3) | ||
| 6,5 | 1 ( 4.5) | 2 ( 9.5) | ||
| 7 | 9 (40.9) | 7 ( 33.3) | ||
| 7,5 | 2 ( 9.1) | 6 ( 28.6) | ||
| 8 | 1 ( 4.5) | 0 ( 0.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_BR_giltinsage_NO (%) | 0.771 | |||
| 0 | 2 ( 9.1) | 1 ( 4.8) | ||
| 2ndheat | 1 ( 4.5) | 0 ( 0.0) | ||
| 6 | 1 ( 4.5) | 0 ( 0.0) | ||
| 7 | 1 ( 4.5) | 1 ( 4.8) | ||
| 7,5 | 1 ( 4.5) | 1 ( 4.8) | ||
| 8 | 12 (54.5) | 13 ( 61.9) | ||
| 8,5 | 1 ( 4.5) | 3 ( 14.3) | ||
| 9 | 1 ( 4.5) | 0 ( 0.0) | ||
| 9,5 | 2 ( 9.1) | 1 ( 4.8) | ||
| noinfo | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_BR_heatgroup_NO (%) | 0.426 | |||
| 0 | 3 (13.6) | 6 ( 28.6) | ||
| 1 | 18 (81.8) | 13 ( 61.9) | ||
| no | 0 ( 0.0) | 1 ( 4.8) | ||
| noinfo | 1 ( 4.5) | 1 ( 4.8) | ||
| MG_BR_heatmarkback_NO (%) | 0.044 | |||
| 0 | 9 (40.9) | 2 ( 9.5) | ||
| 1 | 13 (59.1) | 18 ( 85.7) | ||
| noinfo | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_BR_artinspro_050_5099_100 (%) | 0.666 | |||
| 0 | 1 ( 4.5) | 1 ( 4.8) | ||
| 1 | 8 (36.4) | 5 ( 23.8) | ||
| 2 | 13 (59.1) | 15 ( 71.4) | ||
| MG_BR_once_012 (%) | 0.894 | |||
| 0 | 7 (33.3) | 8 ( 40.0) | ||
| 1 | 12 (57.1) | 10 ( 50.0) | ||
| 2 | 2 ( 9.5) | 2 ( 10.0) | ||
| MG_BR_instriple_NO (%) | 0.432 | |||
| 0 | 7 (31.8) | 4 ( 19.0) | ||
| 1 | 2 ( 9.1) | 1 ( 4.8) | ||
| 10 | 4 (18.2) | 7 ( 33.3) | ||
| 15 | 0 ( 0.0) | 2 ( 9.5) | ||
| 2 | 1 ( 4.5) | 0 ( 0.0) | ||
| 3 | 2 ( 9.1) | 0 ( 0.0) | ||
| 30 | 0 ( 0.0) | 1 ( 4.8) | ||
| 33 | 0 ( 0.0) | 1 ( 4.8) | ||
| 5 | 5 (22.7) | 4 ( 19.0) | ||
| noinfo | 1 ( 4.5) | 1 ( 4.8) | ||
| MG_BR_triple_012 (%) | 0.175 | |||
| 0 | 7 (31.8) | 4 ( 19.0) | ||
| 1 | 14 (63.6) | 12 ( 57.1) | ||
| 2 | 0 ( 0.0) | 4 ( 19.0) | ||
| noinfo | 1 ( 4.5) | 1 ( 4.8) | ||
| MG_BR_nopregus (%) | 0.099 | |||
| 0 | 7 (31.8) | 6 ( 28.6) | ||
| 1 | 14 (63.6) | 8 ( 38.1) | ||
| 2 | 1 ( 4.5) | 6 ( 28.6) | ||
| noinfo | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_BR_bedtype_NO (%) | 0.529 | |||
| 0 | 8 (36.4) | 10 ( 47.6) | ||
| 1 | 6 (27.3) | 5 ( 23.8) | ||
| 2 | 4 (18.2) | 1 ( 4.8) | ||
| 5 | 0 ( 0.0) | 1 ( 4.8) | ||
| 12 | 1 ( 4.5) | 3 ( 14.3) | ||
| 14 | 1 ( 4.5) | 1 ( 4.8) | ||
| 25 | 1 ( 4.5) | 0 ( 0.0) | ||
| 125 | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_BR_bedny = 1 (%) | 14 (63.6) | 11 ( 52.4) | 0.661 | |
| MG_BR_amount (%) | 0.294 | |||
| 0 | 2 ( 9.1) | 5 ( 23.8) | ||
| 1 | 2 ( 9.1) | 4 ( 19.0) | ||
| 2 | 5 (22.7) | 1 ( 4.8) | ||
| 3 | 7 (31.8) | 5 ( 23.8) | ||
| 4 | 6 (27.3) | 6 ( 28.6) | ||
| MG_BR_rootny = 1 (%) | 14 (63.6) | 11 ( 52.4) | 0.661 | |
| MG_BR_toyny (%) | 0.450 | |||
| 0 | 14 (63.6) | 10 ( 47.6) | ||
| 1 | 6 (27.3) | 10 ( 47.6) | ||
| 4 | 1 ( 4.5) | 0 ( 0.0) | ||
| y | 1 ( 4.5) | 1 ( 4.8) | ||
| MG_BR_animdirtmed = 2 (%) | 8 (42.1) | 9 ( 45.0) | 1.000 | |
| MG_BR_feedtype (%) | 0.115 | |||
| 1 | 1 ( 4.5) | 0 ( 0.0) | ||
| 2 | 2 ( 9.1) | 0 ( 0.0) | ||
| 3 | 3 (13.6) | 0 ( 0.0) | ||
| 4 | 16 (72.7) | 20 ( 95.2) | ||
| 25 | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_BR_feedclean (%) | 0.563 | |||
| 0 | 18 (81.8) | 17 ( 81.0) | ||
| 1 | 3 (13.6) | 4 ( 19.0) | ||
| no | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_BR_calm (%) | 0.513 | |||
| 0 | 0 ( 0.0) | 1 ( 4.8) | ||
| 1 | 20 (90.9) | 19 ( 90.5) | ||
| 2 | 2 ( 9.1) | 1 ( 4.8) | ||
| MG_BR_dirtanimmed = 2 (%) | 11 (55.0) | 8 ( 40.0) | 0.527 | |
| MG_BR_ster = 1 (%) | 2 ( 9.1) | 4 ( 19.0) | 0.616 | |
| OUT_SOW_mort_dic = 1 (%) | 10 (45.5) | 10 ( 47.6) | 1.000 | |
| OUT_SOW_totrem_dic = 1 (%) | 5 (22.7) | 17 ( 81.0) | <0.001 | |
| OUT_SOW_cull_dic = 1 (%) | 0 ( 0.0) | 21 (100.0) | <0.001 |
Yhteenveto joku hylkays mukaan
res_mca = MCA(medmca, quanti.sup = c(18,19),quali.sup=c(16:17), graph = FALSE)summary(res_mca)##
## Call:
## MCA(X = medmca, quanti.sup = c(18, 19), quali.sup = c(16:17),
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6
## Variance 0.295 0.205 0.185 0.161 0.145 0.134
## % of var. 13.013 9.060 8.162 7.107 6.387 5.933
## Cumulative % of var. 13.013 22.073 30.234 37.341 43.728 49.661
## Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Dim.12
## Variance 0.110 0.104 0.099 0.095 0.088 0.078
## % of var. 4.873 4.605 4.376 4.182 3.884 3.452
## Cumulative % of var. 54.534 59.139 63.515 67.698 71.581 75.034
## Dim.13 Dim.14 Dim.15 Dim.16 Dim.17 Dim.18
## Variance 0.069 0.059 0.056 0.050 0.047 0.044
## % of var. 3.024 2.615 2.481 2.212 2.062 1.955
## Cumulative % of var. 78.058 80.673 83.154 85.366 87.428 89.383
## Dim.19 Dim.20 Dim.21 Dim.22 Dim.23 Dim.24
## Variance 0.038 0.035 0.030 0.028 0.021 0.020
## % of var. 1.682 1.533 1.337 1.217 0.944 0.865
## Cumulative % of var. 91.065 92.598 93.935 95.152 96.097 96.962
## Dim.25 Dim.26 Dim.27 Dim.28 Dim.29 Dim.30
## Variance 0.019 0.013 0.011 0.009 0.006 0.004
## % of var. 0.821 0.564 0.480 0.414 0.274 0.183
## Cumulative % of var. 97.783 98.347 98.827 99.240 99.515 99.698
## Dim.31 Dim.32 Dim.33 Dim.34
## Variance 0.004 0.002 0.001 0.000
## % of var. 0.157 0.091 0.055 0.000
## Cumulative % of var. 99.855 99.945 100.000 100.000
##
## Individuals (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr
## 1 | 0.462 1.682 0.090 | -0.792 7.099
## 2 | -0.654 3.372 0.109 | 0.231 0.603
## 3 | -0.511 2.056 0.063 | 0.265 0.797
## 4 | 0.372 1.091 0.128 | -0.389 1.714
## 5 | 0.870 5.966 0.234 | -0.272 0.835
## 6 | -0.688 3.727 0.343 | 0.145 0.238
## 7 | -0.241 0.457 0.021 | -0.470 2.502
## 8 | 0.145 0.165 0.014 | 0.207 0.485
## 9 | 0.105 0.087 0.009 | -0.136 0.209
## 10 | 0.497 1.951 0.145 | -0.459 2.384
## cos2 Dim.3 ctr cos2
## 1 0.265 | 0.240 0.726 0.024 |
## 2 0.014 | 0.116 0.171 0.003 |
## 3 0.017 | -0.604 4.586 0.088 |
## 4 0.140 | -0.116 0.168 0.012 |
## 5 0.023 | -0.415 2.166 0.053 |
## 6 0.015 | 0.116 0.170 0.010 |
## 7 0.082 | 0.452 2.564 0.075 |
## 8 0.029 | -0.210 0.553 0.030 |
## 9 0.016 | 0.271 0.926 0.062 |
## 10 0.123 | -0.103 0.134 0.006 |
##
## Categories (the 10 first)
## Dim.1 ctr cos2 v.test Dim.2
## MG_BR_artinspro_050_5099_100_0 | -1.230 1.589 0.074 -1.760 | 0.305
## MG_BR_artinspro_050_5099_100_1 | -0.685 3.206 0.203 -2.922 | -0.059
## MG_BR_artinspro_050_5099_100_2 | 0.406 2.424 0.307 3.593 | 0.006
## MG_BR_once_012_0 | 0.198 0.308 0.021 0.938 | -0.164
## MG_BR_once_012_1 | -0.244 0.688 0.062 -1.618 | -0.148
## MG_BR_once_012_2 | -0.536 0.603 0.029 -1.112 | -0.274
## MG_BR_once_012_Not Assigned | 2.271 5.422 0.252 3.250 | 3.412
## MG_BR_triple_012_0 | -0.531 1.631 0.097 -2.018 | -0.087
## MG_BR_triple_012_1 | -0.018 0.004 0.000 -0.144 | -0.151
## MG_BR_triple_012_2 | 0.442 0.411 0.020 0.917 | -0.486
## ctr cos2 v.test Dim.3 ctr cos2
## MG_BR_artinspro_050_5099_100_0 0.141 0.005 0.437 | 2.238 8.393 0.244
## MG_BR_artinspro_050_5099_100_1 0.035 0.002 -0.253 | 0.576 3.617 0.144
## MG_BR_artinspro_050_5099_100_2 0.001 0.000 0.051 | -0.427 4.285 0.341
## MG_BR_once_012_0 0.305 0.014 -0.778 | -0.588 4.350 0.185
## MG_BR_once_012_1 0.366 0.023 -0.984 | 0.115 0.245 0.014
## MG_BR_once_012_2 0.227 0.008 -0.570 | 1.247 5.211 0.159
## MG_BR_once_012_Not Assigned 17.575 0.568 4.883 | 0.651 0.709 0.021
## MG_BR_triple_012_0 0.063 0.003 -0.330 | 0.707 4.609 0.172
## MG_BR_triple_012_1 0.447 0.035 -1.210 | -0.371 2.991 0.210
## MG_BR_triple_012_2 0.713 0.024 -1.008 | 0.139 0.064 0.002
## v.test
## MG_BR_artinspro_050_5099_100_0 3.203 |
## MG_BR_artinspro_050_5099_100_1 2.458 |
## MG_BR_artinspro_050_5099_100_2 -3.784 |
## MG_BR_once_012_0 -2.790 |
## MG_BR_once_012_1 0.764 |
## MG_BR_once_012_2 2.588 |
## MG_BR_once_012_Not Assigned 0.931 |
## MG_BR_triple_012_0 2.687 |
## MG_BR_triple_012_1 -2.970 |
## MG_BR_triple_012_2 0.288 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## MG_BR_artinspro_050_5099_100 | 0.319 0.005 0.452 |
## MG_BR_once_012 | 0.311 0.569 0.292 |
## MG_BR_triple_012 | 0.330 0.579 0.232 |
## MG_BR_nopregus | 0.477 0.441 0.112 |
## MG_BR_bedny | 0.696 0.072 0.021 |
## MG_BR_amount | 0.699 0.132 0.132 |
## MG_BR_rootny | 0.439 0.136 0.021 |
## MG_BR_toyny | 0.197 0.125 0.487 |
## MG_BR_animdirtmed | 0.129 0.254 0.141 |
## MG_BR_feedtype | 0.232 0.035 0.448 |
##
## Supplementary categories
## Dim.1 cos2 v.test Dim.2 cos2
## OUT_SOW_totrem_dic_0 | -0.301 0.087 -1.909 | 0.166 0.026
## OUT_SOW_totrem_dic_1 | 0.288 0.087 1.909 | -0.158 0.026
## OUT_SOW_cull_dic_0 | -0.161 0.027 -1.067 | 0.007 0.000
## OUT_SOW_cull_dic_1 | 0.169 0.027 1.067 | -0.007 0.000
## v.test Dim.3 cos2 v.test
## OUT_SOW_totrem_dic_0 1.051 | -0.131 0.016 -0.828 |
## OUT_SOW_totrem_dic_1 -1.051 | 0.125 0.016 0.828 |
## OUT_SOW_cull_dic_0 0.046 | 0.076 0.006 0.502 |
## OUT_SOW_cull_dic_1 -0.046 | -0.079 0.006 -0.502 |
##
## Supplementary categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## OUT_SOW_totrem_dic | 0.087 0.026 0.016 |
## OUT_SOW_cull_dic | 0.027 0.000 0.006 |
##
## Supplementary continuous variables
## Dim.1 Dim.2 Dim.3
## OUT_SOW_mort_proNUM | 0.348 | -0.003 | 0.289 |
## OUT_SOW_cullproNUM | 0.232 | -0.045 | 0.051 |To visualize the percentage of inertia explained by each MCA dimension:
eig.val <- res_mca$eig
barplot(eig.val[, 2],
names.arg = 1:nrow(eig.val),
main = "Variances Explained by Dimensions (%)",
xlab = "Principal Dimensions",
ylab = "Percentage of variances",
col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2],
type = "b", pch = 19, col = "red")
res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
var_sup = TRUE, var_lab_min_contrib = 0,
col_var = "Variable", symbol_var = "Type",
size_var = NULL, size_range = c(10, 300),
labels_size = 10, point_size = 56,
transitions = TRUE, labels_positions = NULL)res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
lab_var = NULL, , ind_lab_min_contrib = 0,
col_var = NULL, labels_size = 9,
point_opacity = 0.5, opacity_var = NULL, point_size = 64,
ellipses = FALSE, transitions = TRUE, labels_positions = NULL)fviz_mca_var(res_mca, choice = "quanti.sup",
ggtheme = theme_minimal())
## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```
##
## ```
## drawn <-
## c("24", "21", "27", "39", "23", "32", "6", "1", "35", "13")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
##
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
##
##
## The cluster 1 is made of individuals such as*. This group is characterized by6* and *6*. :
##
## - high frequency for the factors *MG_BR_bedny=MG_BR_bedny_1*, *MG_BR_rootny=MG_BR_rootny_1*, *MG_BR_nopregus=MG_BR_nopregus_0*, *MG_BR_amount=MG_BR_amount_3*, *MG_BR_amount=MG_BR_amount_2*, *MG_BR_amount=MG_BR_amount_1* and *MG_BR_ster=MG_BR_ster_0* (factors are sorted from the most common).
## - low frequency for the factors *MG_BR_bedny=MG_BR_bedny_0*, *MG_BR_amount=MG_BR_amount_4*, *MG_BR_rootny=MG_BR_rootny_0*, *MG_BR_nopregus=MG_BR_nopregus_1*, *MG_BR_feedtype=MG_BR_feedtype_4*, *MG_BR_amount=MG_BR_amount_0*, *MG_BR_artinspro_050_5099_100=MG_BR_artinspro_050_5099_100_2* and *MG_BR_ster=MG_BR_ster_1* (factors are sorted from the rarest).
##
## The cluster 2 is made of individuals such as*. This group is characterized by1* and *1*. :
##
## - high frequency for the factors *MG_BR_bedny=MG_BR_bedny_0*, *MG_BR_rootny=MG_BR_rootny_0*, *MG_BR_amount=MG_BR_amount_4*, *MG_BR_nopregus=MG_BR_nopregus_1*, *MG_BR_amount=MG_BR_amount_0*, *MG_BR_ster=MG_BR_ster_1*, *MG_BR_feedtype=MG_BR_feedtype_4* and *MG_BR_dirtanimmed=MG_BR_dirtanimmed_Not Assigned* (factors are sorted from the most common).
## - low frequency for the factors *MG_BR_bedny=MG_BR_bedny_1*, *MG_BR_rootny=MG_BR_rootny_1*, *MG_BR_nopregus=MG_BR_nopregus_0*, *MG_BR_amount=MG_BR_amount_3*, *MG_BR_ster=MG_BR_ster_0*, *MG_BR_amount=MG_BR_amount_1* and *MG_BR_amount=MG_BR_amount_2* (factors are sorted from the rarest).
##
## The cluster 3 is made of individuals such as*. This group is characterized by21* and *21*. :
##
## - high frequency for the factors *MG_BR_triple_012=MG_BR_triple_012_noinfo*, *MG_BR_once_012=MG_BR_once_012_Not Assigned*, *MG_BR_feedclean=MG_BR_feedclean_1* and *MG_BR_nopregus=MG_BR_nopregus_noinfo* (factors are sorted from the most common).
## - low frequency for the factor **.## **Results for the Hierarchical Clustering on Principal Components**
## name
## 1 "$data.clust"
## 2 "$desc.var"
## 3 "$desc.var$test.chi2"
## 4 "$desc.axes$category"
## 5 "$desc.axes"
## 6 "$desc.axes$quanti.var"
## 7 "$desc.axes$quanti"
## 8 "$desc.ind"
## 9 "$desc.ind$para"
## 10 "$desc.ind$dist"
## 11 "$call"
## 12 "$call$t"
## description
## 1 "dataset with the cluster of the individuals"
## 2 "description of the clusters by the variables"
## 3 "description of the cluster var. by the categorical var."
## 4 "description of the clusters by the categories."
## 5 "description of the clusters by the dimensions"
## 6 "description of the cluster var. by the axes"
## 7 "description of the clusters by the axes"
## 8 "description of the clusters by the individuals"
## 9 "parangons of each clusters"
## 10 "specific individuals"
## 11 "summary statistics"
## 12 "description of the tree"Gestation management
# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="managpr.csv", header=TRUE)glimpse(med)## Observations: 43
## Variables: 19
## $ MG_PR_earlyHAR_kaNUM <fctr> 0,9, 0,15, 0, 0,2, 1, 0,05, 0,2, 0,...
## $ MG_PR_type <int> 2, 1, 12, 13, 2, 1, 2, 1, 14, 1, 13,...
## $ MG_PR_rootyn <int> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ MG_PR_toyyn <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...
## $ MG_PR_toy <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, ...
## $ MG_PR_kuivaliete <int> 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, ...
## $ MG_PR_ruok_0nonlock_1lock <int> 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...
## $ MG_PR_feedtype <int> 3, 1, 25, 4, 5, 4, 4, 5, 4, 4, 4, 5,...
## $ MG_PR_calm <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ MG_PR_dirt_NUM_NO <int> 20, 0, 10, 20, 20, 20, 20, 10, 20, 1...
## $ MG_PR_animdirtmed <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, ...
## $ MG_PR_ster <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...
## $ MG_PR_late_HAR_kaNUM_NO <fctr> 0,9, 0,15, 0, 0,1, , , 0,2, 0,6, 0,...
## $ OUT_SOW_mort_proNUM <int> 5, 5, 8, 27, 10, 0, 17, 13, 0, 6, 9,...
## $ OUT_SOW_mort_dic <int> 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, ...
## $ OUT_SOW_totremproNUM <int> 34, 38, 53, 57, 65, 64, 47, 44, 24, ...
## $ OUT_SOW_totrem_dic <int> 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, ...
## $ OUT_SOW_cullproNUM <int> 29, 33, 45, 30, 55, 64, 30, 31, 24, ...
## $ OUT_SOW_cull_dic <int> 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, ...med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM ## [1] 29 33 45 30 55 64 30 31 24 28 29 36 70 27 42 22 40
## [18] 29 38 38 27 30 44 45 34 45 41 35 49 32 39 80 14 24
## [35] 35 56 39 50 42 35 33 107 42
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107colnames(med)=="OUT_SOW_cullproNUM"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSEcolnames(med)=="OUT_SOWmortpro"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEmed$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="grey") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+ scale_fill_manual("key")
library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")
library(tableone)
KreateTableOne = function(x, ...){
t1 = tableone::CreateTableOne(data=x, ...)
t2 = print(t1, quote=TRUE)
rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
return(t2)
}Yhteenveto kuolleisuuden mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')table1%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 23 | 20 | ||
| MG_PR_dirt_NUM_NO (mean (sd)) | 3.00 (1.95) | 4.26 (2.38) | 0.066 | |
| MG_PR_late_HAR_kaNUM_NO (mean (sd)) | 4.87 (4.70) | 5.95 (5.57) | 0.494 | |
| MG_PR_earlyHAR_kaNUM (mean (sd)) | 7.91 (4.73) | 10.15 (5.41) | 0.156 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 4.74 (2.12) | 13.35 (3.27) | <0.001 | |
| OUT_SOW_totremproNUM (mean (sd)) | 8.91 (5.80) | 17.85 (8.03) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 11.78 (6.69) | 15.35 (8.20) | 0.124 | |
| MG_PR_type (%) | 0.295 | |||
| 1 | 8 (34.8) | 4 ( 20.0) | ||
| 2 | 8 (34.8) | 10 ( 50.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.0) | ||
| 12 | 3 (13.0) | 0 ( 0.0) | ||
| 13 | 3 (13.0) | 2 ( 10.0) | ||
| 14 | 1 ( 4.3) | 0 ( 0.0) | ||
| 23 | 0 ( 0.0) | 1 ( 5.0) | ||
| 123 | 0 ( 0.0) | 1 ( 5.0) | ||
| 124 | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_PR_rootyn = 1 (%) | 20 (87.0) | 16 ( 80.0) | 0.840 | |
| MG_PR_toyyn = 1 (%) | 7 (30.4) | 9 ( 45.0) | 0.503 | |
| MG_PR_toy (%) | 0.270 | |||
| 0 | 16 (69.6) | 11 ( 55.0) | ||
| 1 | 0 ( 0.0) | 1 ( 5.0) | ||
| 2 | 3 (13.0) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.0) | ||
| 4 | 4 (17.4) | 4 ( 20.0) | ||
| 5 | 0 ( 0.0) | 1 ( 5.0) | ||
| 14 | 0 ( 0.0) | 1 ( 5.0) | ||
| 24 | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_PR_kuivaliete (%) | 0.133 | |||
| 1 | 7 (30.4) | 6 ( 30.0) | ||
| 2 | 12 (52.2) | 14 ( 70.0) | ||
| 12 | 4 (17.4) | 0 ( 0.0) | ||
| MG_PR_ruok_0nonlock_1lock (%) | 0.248 | |||
| 0 | 10 (43.5) | 12 ( 60.0) | ||
| 1 | 13 (56.5) | 7 ( 35.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_PR_feedtype (%) | 0.354 | |||
| 1 | 2 ( 8.7) | 1 ( 5.0) | ||
| 2 | 1 ( 4.3) | 4 ( 20.0) | ||
| 3 | 4 (17.4) | 4 ( 20.0) | ||
| 4 | 13 (56.5) | 7 ( 35.0) | ||
| 5 | 1 ( 4.3) | 3 ( 15.0) | ||
| 6 | 0 ( 0.0) | 1 ( 5.0) | ||
| 25 | 1 ( 4.3) | 0 ( 0.0) | ||
| 34 | 1 ( 4.3) | 0 ( 0.0) | ||
| MG_PR_calm = 2 (%) | 1 ( 4.3) | 2 ( 10.0) | 0.900 | |
| MG_PR_animdirtmed = 2 (%) | 6 (26.1) | 9 ( 47.4) | 0.267 | |
| MG_PR_ster = 1 (%) | 2 ( 8.7) | 2 ( 10.0) | 1.000 | |
| OUT_SOW_mort_dic = 1 (%) | 0 ( 0.0) | 20 (100.0) | <0.001 | |
| OUT_SOW_totrem_dic = 1 (%) | 7 (30.4) | 15 ( 75.0) | 0.009 | |
| OUT_SOW_cull_dic = 1 (%) | 11 (47.8) | 10 ( 50.0) | 1.000 |
Yhteenveto poistojen mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')table2%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 22 | 21 | ||
| MG_PR_dirt_NUM_NO (mean (sd)) | 3.33 (1.93) | 3.81 (2.50) | 0.494 | |
| MG_PR_late_HAR_kaNUM_NO (mean (sd)) | 6.41 (4.96) | 4.29 (5.11) | 0.174 | |
| MG_PR_earlyHAR_kaNUM (mean (sd)) | 8.59 (4.56) | 9.33 (5.74) | 0.640 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 8.73 (5.55) | 8.76 (4.73) | 0.983 | |
| OUT_SOW_totremproNUM (mean (sd)) | 7.77 (5.46) | 18.62 (6.82) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 7.18 (3.57) | 20.00 (4.27) | <0.001 | |
| MG_PR_type (%) | 0.640 | |||
| 1 | 5 (22.7) | 7 ( 33.3) | ||
| 2 | 10 (45.5) | 8 ( 38.1) | ||
| 3 | 0 ( 0.0) | 1 ( 4.8) | ||
| 12 | 1 ( 4.5) | 2 ( 9.5) | ||
| 13 | 3 (13.6) | 2 ( 9.5) | ||
| 14 | 1 ( 4.5) | 0 ( 0.0) | ||
| 23 | 1 ( 4.5) | 0 ( 0.0) | ||
| 123 | 0 ( 0.0) | 1 ( 4.8) | ||
| 124 | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_PR_rootyn = 1 (%) | 20 (90.9) | 16 ( 76.2) | 0.372 | |
| MG_PR_toyyn = 1 (%) | 6 (27.3) | 10 ( 47.6) | 0.287 | |
| MG_PR_toy (%) | 0.456 | |||
| 0 | 16 (72.7) | 11 ( 52.4) | ||
| 1 | 0 ( 0.0) | 1 ( 4.8) | ||
| 2 | 1 ( 4.5) | 2 ( 9.5) | ||
| 3 | 0 ( 0.0) | 1 ( 4.8) | ||
| 4 | 3 (13.6) | 5 ( 23.8) | ||
| 5 | 1 ( 4.5) | 0 ( 0.0) | ||
| 14 | 0 ( 0.0) | 1 ( 4.8) | ||
| 24 | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_PR_kuivaliete (%) | 0.663 | |||
| 1 | 8 (36.4) | 5 ( 23.8) | ||
| 2 | 12 (54.5) | 14 ( 66.7) | ||
| 12 | 2 ( 9.1) | 2 ( 9.5) | ||
| MG_PR_ruok_0nonlock_1lock (%) | 0.560 | |||
| 0 | 12 (54.5) | 10 ( 47.6) | ||
| 1 | 10 (45.5) | 10 ( 47.6) | ||
| 3 | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_PR_feedtype (%) | 0.646 | |||
| 1 | 1 ( 4.5) | 2 ( 9.5) | ||
| 2 | 4 (18.2) | 1 ( 4.8) | ||
| 3 | 4 (18.2) | 4 ( 19.0) | ||
| 4 | 10 (45.5) | 10 ( 47.6) | ||
| 5 | 2 ( 9.1) | 2 ( 9.5) | ||
| 6 | 0 ( 0.0) | 1 ( 4.8) | ||
| 25 | 0 ( 0.0) | 1 ( 4.8) | ||
| 34 | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_PR_calm = 2 (%) | 0 ( 0.0) | 3 ( 14.3) | 0.215 | |
| MG_PR_animdirtmed = 2 (%) | 6 (28.6) | 9 ( 42.9) | 0.520 | |
| MG_PR_ster = 1 (%) | 2 ( 9.1) | 2 ( 9.5) | 1.000 | |
| OUT_SOW_mort_dic = 1 (%) | 10 (45.5) | 10 ( 47.6) | 1.000 | |
| OUT_SOW_totrem_dic = 1 (%) | 5 (22.7) | 17 ( 81.0) | <0.001 | |
| OUT_SOW_cull_dic = 1 (%) | 0 ( 0.0) | 21 (100.0) | <0.001 |
Yhteenveto joku hylkays mukaan
res_mca = MCA(medmca, quanti.sup = c(14,15),quali.sup=c(12:13), graph = FALSE)summary(res_mca)##
## Call:
## MCA(X = medmca, quanti.sup = c(14, 15), quali.sup = c(12:13),
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6
## Variance 0.351 0.274 0.237 0.218 0.211 0.201
## % of var. 11.689 9.130 7.898 7.281 7.034 6.704
## Cumulative % of var. 11.689 20.819 28.718 35.998 43.032 49.737
## Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Dim.12
## Variance 0.172 0.164 0.157 0.122 0.119 0.112
## % of var. 5.749 5.459 5.238 4.078 3.957 3.741
## Cumulative % of var. 55.486 60.945 66.183 70.261 74.218 77.958
## Dim.13 Dim.14 Dim.15 Dim.16 Dim.17 Dim.18
## Variance 0.100 0.093 0.091 0.080 0.060 0.053
## % of var. 3.347 3.101 3.030 2.654 1.990 1.765
## Cumulative % of var. 81.306 84.406 87.437 90.091 92.081 93.846
## Dim.19 Dim.20 Dim.21 Dim.22 Dim.23 Dim.24
## Variance 0.043 0.036 0.032 0.026 0.015 0.013
## % of var. 1.427 1.214 1.055 0.866 0.485 0.418
## Cumulative % of var. 95.273 96.486 97.541 98.407 98.893 99.311
## Dim.25 Dim.26 Dim.27 Dim.28 Dim.29 Dim.30
## Variance 0.011 0.010 0.000 0.000 0.000 0.000
## % of var. 0.369 0.320 0.000 0.000 0.000 0.000
## Cumulative % of var. 99.680 100.000 100.000 100.000 100.000 100.000
## Dim.31 Dim.32 Dim.33
## Variance 0.000 0.000 0.000
## % of var. 0.000 0.000 0.000
## Cumulative % of var. 100.000 100.000 100.000
##
## Individuals (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | 0.089 0.052 0.008 | -0.061 0.032 0.004 |
## 2 | -0.526 1.832 0.137 | 0.335 0.953 0.056 |
## 3 | -0.702 3.272 0.088 | 0.358 1.086 0.023 |
## 4 | -0.263 0.460 0.041 | -0.433 1.595 0.110 |
## 5 | 0.155 0.160 0.017 | 0.390 1.294 0.104 |
## 6 | -0.741 3.637 0.591 | 0.015 0.002 0.000 |
## 7 | -0.207 0.284 0.062 | -0.142 0.170 0.029 |
## 8 | -0.250 0.414 0.036 | 0.583 2.881 0.198 |
## 9 | -0.606 2.436 0.084 | -0.203 0.351 0.009 |
## 10 | -0.741 3.637 0.591 | 0.015 0.002 0.000 |
## Dim.3 ctr cos2
## 1 -0.040 0.015 0.002 |
## 2 -0.367 1.319 0.067 |
## 3 -0.142 0.199 0.004 |
## 4 0.397 1.546 0.092 |
## 5 0.274 0.736 0.051 |
## 6 0.005 0.000 0.000 |
## 7 0.240 0.564 0.083 |
## 8 0.047 0.022 0.001 |
## 9 0.401 1.575 0.037 |
## 10 0.005 0.000 0.000 |
##
## Categories (the 10 first)
## Dim.1 ctr cos2 v.test Dim.2 ctr cos2
## MG_PR_type_1 | -0.776 4.354 0.233 -3.128 | 0.324 0.974 0.041
## MG_PR_type_12 | -1.067 2.061 0.085 -1.895 | 0.259 0.155 0.005
## MG_PR_type_123 | 2.047 2.526 0.100 2.047 | 3.754 10.877 0.336
## MG_PR_type_124 | 1.404 1.189 0.047 1.404 | 1.063 0.873 0.027
## MG_PR_type_13 | -0.416 0.522 0.023 -0.978 | -0.667 1.718 0.059
## MG_PR_type_14 | -1.023 0.632 0.025 -1.023 | -0.388 0.116 0.004
## MG_PR_type_2 | 0.741 5.954 0.395 4.073 | -0.240 0.802 0.042
## MG_PR_type_23 | -0.987 0.588 0.023 -0.987 | -0.559 0.241 0.007
## MG_PR_type_3 | -0.181 0.020 0.001 -0.181 | -0.878 0.595 0.018
## MG_PR_rootyn_0 | 0.661 1.847 0.085 1.890 | -1.050 5.952 0.214
## v.test Dim.3 ctr cos2 v.test
## MG_PR_type_1 1.308 | -0.515 2.841 0.103 -2.077 |
## MG_PR_type_12 0.460 | 0.033 0.003 0.000 0.059 |
## MG_PR_type_123 3.754 | 3.481 10.810 0.288 3.481 |
## MG_PR_type_124 1.063 | -1.063 1.008 0.027 -1.063 |
## MG_PR_type_13 -1.569 | 0.331 0.488 0.014 0.777 |
## MG_PR_type_14 -0.388 | 0.823 0.604 0.016 0.823 |
## MG_PR_type_2 -1.321 | -0.079 0.101 0.005 -0.436 |
## MG_PR_type_23 -0.559 | 1.921 3.293 0.088 1.921 |
## MG_PR_type_3 -0.878 | 0.692 0.427 0.011 0.692 |
## MG_PR_rootyn_0 -2.999 | 0.361 0.813 0.025 1.031 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## MG_PR_type | 0.688 0.493 0.510 |
## MG_PR_rootyn | 0.085 0.214 0.025 |
## MG_PR_toyyn | 0.487 0.006 0.062 |
## MG_PR_toy | 0.621 0.762 0.740 |
## MG_PR_kuivaliete | 0.275 0.069 0.132 |
## MG_PR_ruok_0nonlock_1lock | 0.458 0.538 0.132 |
## MG_PR_feedtype | 0.613 0.556 0.656 |
## MG_PR_calm | 0.093 0.156 0.020 |
## MG_PR_animdirtmed | 0.160 0.008 0.192 |
## MG_PR_ster | 0.202 0.199 0.137 |
##
## Supplementary categories
## Dim.1 cos2 v.test Dim.2 cos2 v.test
## OUT_SOW_totrem_dic_0 | -0.343 0.112 -2.173 | 0.071 0.005 0.451 |
## OUT_SOW_totrem_dic_1 | 0.328 0.112 2.173 | -0.068 0.005 -0.451 |
## OUT_SOW_cull_dic_0 | -0.145 0.022 -0.962 | 0.074 0.006 0.493 |
## OUT_SOW_cull_dic_1 | 0.152 0.022 0.962 | -0.078 0.006 -0.493 |
## Dim.3 cos2 v.test
## OUT_SOW_totrem_dic_0 -0.038 0.001 -0.240 |
## OUT_SOW_totrem_dic_1 0.036 0.001 0.240 |
## OUT_SOW_cull_dic_0 0.026 0.001 0.172 |
## OUT_SOW_cull_dic_1 -0.027 0.001 -0.172 |
##
## Supplementary categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## OUT_SOW_totrem_dic | 0.112 0.005 0.001 |
## OUT_SOW_cull_dic | 0.022 0.006 0.001 |
##
## Supplementary continuous variables
## Dim.1 Dim.2 Dim.3
## OUT_SOW_mort_proNUM | 0.307 | 0.043 | -0.070 |
## OUT_SOW_cullproNUM | 0.240 | -0.071 | 0.029 |To visualize the percentage of inertia explained by each MCA dimension:
eig.val <- res_mca$eig
barplot(eig.val[, 2],
names.arg = 1:nrow(eig.val),
main = "Variances Explained by Dimensions (%)",
xlab = "Principal Dimensions",
ylab = "Percentage of variances",
col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2],
type = "b", pch = 19, col = "red")
res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
var_sup = TRUE, var_lab_min_contrib = 0,
col_var = "Variable", symbol_var = "Type",
size_var = NULL, size_range = c(10, 300),
labels_size = 10, point_size = 56,
transitions = TRUE, labels_positions = NULL)res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
lab_var = NULL, , ind_lab_min_contrib = 0,
col_var = NULL, labels_size = 9,
point_opacity = 0.5, opacity_var = NULL, point_size = 64,
ellipses = FALSE, transitions = TRUE, labels_positions = NULL)fviz_mca_var(res_mca, choice = "quanti.sup",
ggtheme = theme_minimal())
## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```
##
## ```
## drawn <-
## c("36", "10", "6", "20", "22", "29", "13", "27", "42", "32")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
##
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 7 clusters.*
##
##
## The cluster 1 is made of individuals such as*. This group is characterized by6* and *6*. :
##
## - high frequency for the factors *MG_PR_type=MG_PR_type_1*, *MG_PR_toy=MG_PR_toy_0*, *MG_PR_toyyn=MG_PR_toyyn_0*, *MG_PR_kuivaliete=MG_PR_kuivaliete_1*, *MG_PR_animdirtmed=MG_PR_animdirtmed_1*, *OUT_SOW_mort_dic=OUT_SOW_mort_dic_0*, *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_0* and *MG_PR_type=MG_PR_type_12* (factors are sorted from the most common).
## - low frequency for the factors *MG_PR_type=MG_PR_type_2*, *MG_PR_toyyn=MG_PR_toyyn_1*, *MG_PR_kuivaliete=MG_PR_kuivaliete_2*, *MG_PR_animdirtmed=MG_PR_animdirtmed_2*, *OUT_SOW_mort_dic=OUT_SOW_mort_dic_1*, *MG_PR_toy=MG_PR_toy_4*, *MG_PR_feedtype=MG_PR_feedtype_3* and *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_1* (factors are sorted from the rarest).
##
## The cluster 2 is made of individuals sharing :
##
## - high frequency for the factors *MG_PR_feedtype=MG_PR_feedtype_4*, *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_1*, *MG_PR_type=MG_PR_type_13* and *MG_PR_kuivaliete=MG_PR_kuivaliete_2* (factors are sorted from the most common).
## - low frequency for the factors *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_0*, *MG_PR_kuivaliete=MG_PR_kuivaliete_1* and *MG_PR_type=MG_PR_type_1* (factors are sorted from the rarest).
##
## The cluster 3 is made of individuals sharing :
##
## - high frequency for the factors *MG_PR_feedtype=MG_PR_feedtype_6* and *MG_PR_toy=MG_PR_toy_3* (factors are sorted from the most common).
##
## The cluster 4 is made of individuals such as*. This group is characterized by13* and *13*. :
##
## - high frequency for the factors *MG_PR_type=MG_PR_type_2*, *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_0*, *MG_PR_feedtype=MG_PR_feedtype_3*, *MG_PR_animdirtmed=MG_PR_animdirtmed_2* and *MG_PR_feedtype=MG_PR_feedtype_2* (factors are sorted from the most common).
## - low frequency for the factors *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_1*, *MG_PR_feedtype=MG_PR_feedtype_4*, *MG_PR_animdirtmed=MG_PR_animdirtmed_1* and *MG_PR_type=MG_PR_type_1* (factors are sorted from the rarest).
##
## The cluster 5 is made of individuals sharing :
##
## - high frequency for the factors *MG_PR_toy=MG_PR_toy_24* and *MG_PR_type=MG_PR_type_124* (factors are sorted from the most common).
##
## The 1st cluster is made of individuals such as *29*. This group is characterized by :
##
## - high frequency for the factors *MG_PR_toy=MG_PR_toy_1* and *MG_PR_type=MG_PR_type_123* (factors are sorted from the most common).
##
## The 1st cluster is made of individuals such as *36*. This group is characterized by :
##
## - high frequency for the factors *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_3* and *MG_PR_toy=MG_PR_toy_14* (factors are sorted from the most common).## **Results for the Hierarchical Clustering on Principal Components**
## name
## 1 "$data.clust"
## 2 "$desc.var"
## 3 "$desc.var$test.chi2"
## 4 "$desc.axes$category"
## 5 "$desc.axes"
## 6 "$desc.axes$quanti.var"
## 7 "$desc.axes$quanti"
## 8 "$desc.ind"
## 9 "$desc.ind$para"
## 10 "$desc.ind$dist"
## 11 "$call"
## 12 "$call$t"
## description
## 1 "dataset with the cluster of the individuals"
## 2 "description of the clusters by the variables"
## 3 "description of the cluster var. by the categorical var."
## 4 "description of the clusters by the categories."
## 5 "description of the clusters by the dimensions"
## 6 "description of the cluster var. by the axes"
## 7 "description of the clusters by the axes"
## 8 "description of the clusters by the individuals"
## 9 "parangons of each clusters"
## 10 "specific individuals"
## 11 "summary statistics"
## 12 "description of the tree"Farrowing management
# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="managfar.csv", header=TRUE)glimpse(med)## Observations: 43
## Variables: 31
## $ MG_FAR_ToFarunitNUM_NO <int> 6, 5, 4, 7, 7, 7, 3, 5, 5, 5, ...
## $ MG_FAR_ind_0no_1rout_2sometimes <int> 2, 0, 2, 2, 2, 2, 2, 0, 0, 2, ...
## $ MG_FAR_NestmatdaysNUM_NO <int> 6, 1, 4, 7, 2, 0, 3, 2, 5, 3, ...
## $ MG_FAR_nestmatamount <int> 3, 2, 2, 2, 3, 2, 3, 1, 2, 2, ...
## $ MG_FAR_nestmat_NO <fctr> STR, STR, STR_CUT, heiina , _...
## $ MG_FAR_ox_0_13_46_7 <int> 3, 1, 1, 1, 3, 3, 3, 2, 2, 2, ...
## $ MG_FAR_obstex_preox <fctr> 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,...
## $ MG_FAR_far_assist_CAT <fctr> 50, 20-50, <6, 20-50, noinfo,...
## $ MG_FAR_farassist_MAY_NO <fctr> WASH_GLO_LUBR, WASH_HANDWASH_...
## $ MG_FAR_piglet_rem_ageNUM_NO <fctr> 0,5, 1, 0,5, 0,5, 1, no, 0,5,...
## $ MG_FAR_piglet_rem_amountCAT <fctr> 1, 1, 1, 3, 4, 0, 1, 3, 2, 4,...
## $ MG_FAR_pigletremaount_NUM_NO <fctr> 9, 5, 4, 25, 50, 0, 10, 43, 1...
## $ MG_FAR_piglet_addfeedage <fctr> 7-14, 7-14, <7, <7, <7, 7-14,...
## $ MG_FAR_bed_yn <int> 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...
## $ MG_FAR_bed12345_NO <int> 0, 14, 12, 25, 0, 1, 1, 12, 1,...
## $ MG_FAR_bedamount <int> 4, 3, 3, 3, 4, 2, 3, 2, 3, 4, ...
## $ MG_FAR_root_yn <int> 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ MG_FAR_toy <int> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ MG_FAR_toynum_NO <int> 0, NA, NA, 0, 4, NA, NA, NA, 0...
## $ MG_FAR_rootamount <int> 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...
## $ MG_FAR_dirt_NUM_NO <int> 10, 30, 0, 20, 20, 0, 0, NA, 3...
## $ MG_FAR_dirtmed <int> 1, 2, 1, 2, 2, 1, 1, NA, 2, NA...
## $ MG_FAR_diranim_NUM_NO <int> 10, 20, 0, 10, 15, 20, 30, 10,...
## $ MG_FAR_diranimmed <int> 1, 2, 1, 1, 2, 2, 2, 1, 2, NA,...
## $ MG_FAR_toytoinen_MIKA_NO <fctr> 0, 2, 2, 2, 2, , 2, 2, 1, 2, ...
## $ OUT_SOW_mort_proNUM <int> 5, 5, 8, 27, 10, 0, 17, 13, 0,...
## $ OUT_SOW_mort_dic <int> 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, ...
## $ OUT_SOW_totremproNUM <int> 34, 38, 53, 57, 65, 64, 47, 44...
## $ OUT_SOW_totrem_dic <int> 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, ...
## $ OUT_SOW_cullproNUM <int> 29, 33, 45, 30, 55, 64, 30, 31...
## $ OUT_SOW_cull_dic <int> 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, ...med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM ## [1] 29 33 45 30 55 64 30 31 24 28 29 36 70 27 42 22 40
## [18] 29 38 38 27 30 44 45 34 45 41 35 49 32 39 80 14 24
## [35] 35 56 39 50 42 35 33 107 42
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107colnames(med)=="OUT_SOW_cullproNUM"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSEcolnames(med)=="OUT_SOWmortpro"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEmed$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))
X<-medmca
colnames(X)[ apply(X, 2, anyNA) ]## [1] "MG_FAR_ox_0_13_46_7" "MG_FAR_dirtmed" "MG_FAR_diranimmed"for (i in 1:ncol(X)) {
levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}
X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)
medmca<-X #colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="red") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+ scale_fill_manual("key")
library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")
library(tableone)
KreateTableOne = function(x, ...){
t1 = tableone::CreateTableOne(data=x, ...)
t2 = print(t1, quote=TRUE)
rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
return(t2)
}Yhteenveto kuolleisuuden mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')table1%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 23 | 20 | ||
| MG_FAR_ToFarunitNUM_NO (mean (sd)) | 3.04 (1.49) | 3.10 (1.45) | 0.901 | |
| MG_FAR_NestmatdaysNUM_NO (mean (sd)) | 4.78 (2.35) | 3.95 (2.04) | 0.226 | |
| MG_FAR_piglet_rem_ageNUM_NO (mean (sd)) | 3.70 (1.84) | 3.15 (1.87) | 0.342 | |
| MG_FAR_pigletremaount_NUM_NO (mean (sd)) | 11.26 (7.15) | 13.05 (6.34) | 0.393 | |
| MG_FAR_toynum_NO (mean (sd)) | 4.31 (1.89) | 3.87 (1.73) | 0.524 | |
| MG_FAR_dirt_NUM_NO (mean (sd)) | 2.19 (1.25) | 2.59 (1.66) | 0.405 | |
| MG_FAR_diranim_NUM_NO (mean (sd)) | 2.80 (1.67) | 3.06 (2.10) | 0.679 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 4.74 (2.12) | 13.35 (3.27) | <0.001 | |
| OUT_SOW_totremproNUM (mean (sd)) | 8.91 (5.80) | 17.85 (8.03) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 11.78 (6.69) | 15.35 (8.20) | 0.124 | |
| MG_FAR_ind_0no_1rout_2sometimes (%) | 0.287 | |||
| 0 | 10 (43.5) | 7 ( 35.0) | ||
| 1 | 0 ( 0.0) | 2 ( 10.0) | ||
| 2 | 13 (56.5) | 11 ( 55.0) | ||
| MG_FAR_nestmatamount (%) | 0.984 | |||
| 0 | 3 (13.0) | 2 ( 10.0) | ||
| 1 | 2 ( 8.7) | 2 ( 10.0) | ||
| 2 | 13 (56.5) | 11 ( 55.0) | ||
| 3 | 5 (21.7) | 5 ( 25.0) | ||
| MG_FAR_nestmat_NO (%) | 0.363 | |||
| _CUT | 0 ( 0.0) | 2 ( 10.0) | ||
| _NWS | 3 (13.0) | 2 ( 10.0) | ||
| 0 | 3 (13.0) | 2 ( 10.0) | ||
| heiina | 0 ( 0.0) | 1 ( 5.0) | ||
| heina | 0 ( 0.0) | 1 ( 5.0) | ||
| heina puruturve | 0 ( 0.0) | 1 ( 5.0) | ||
| heina turve | 0 ( 0.0) | 1 ( 5.0) | ||
| no | 0 ( 0.0) | 1 ( 5.0) | ||
| STR | 11 (47.8) | 6 ( 30.0) | ||
| STR_CUT | 5 (21.7) | 2 ( 10.0) | ||
| STR_CUT_NWS | 1 ( 4.3) | 0 ( 0.0) | ||
| STR_NWS | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_FAR_ox_0_13_46_7 (%) | 0.254 | |||
| 0 | 1 ( 4.3) | 0 ( 0.0) | ||
| 1 | 10 (43.5) | 8 ( 42.1) | ||
| 2 | 5 (21.7) | 1 ( 5.3) | ||
| 3 | 7 (30.4) | 10 ( 52.6) | ||
| MG_FAR_obstex_preox (%) | 0.493 | |||
| 0 | 15 (65.2) | 11 ( 55.0) | ||
| 1 | 8 (34.8) | 8 ( 40.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_FAR_far_assist_CAT (%) | 0.301 | |||
| <6 | 4 (17.4) | 7 ( 35.0) | ||
| 10 | 1 ( 4.3) | 0 ( 0.0) | ||
| 15 | 0 ( 0.0) | 1 ( 5.0) | ||
| 20-50 | 4 (17.4) | 4 ( 20.0) | ||
| 50 | 3 (13.0) | 1 ( 5.0) | ||
| 6-20 | 10 (43.5) | 4 ( 20.0) | ||
| noinfo | 1 ( 4.3) | 3 ( 15.0) | ||
| MG_FAR_farassist_MAY_NO (%) | 0.350 | |||
| 0 ( 0.0) | 2 ( 10.0) | |||
| _GLO_LUBR | 8 (34.8) | 7 ( 35.0) | ||
| _HANDWASH_GLO_LUBR | 1 ( 4.3) | 0 ( 0.0) | ||
| GLO_LUBR | 0 ( 0.0) | 1 ( 5.0) | ||
| WASH_GLO_LUBR | 8 (34.8) | 5 ( 25.0) | ||
| WASH_HANDWASH_GLO_LUBR | 4 (17.4) | 5 ( 25.0) | ||
| WASH_HANDWASH_LUBR | 2 ( 8.7) | 0 ( 0.0) | ||
| MG_FAR_piglet_rem_amountCAT (%) | 0.001 | |||
| 0 | 1 ( 4.3) | 0 ( 0.0) | ||
| 1 | 10 (43.5) | 5 ( 25.0) | ||
| 2 | 10 (43.5) | 1 ( 5.0) | ||
| 3 | 0 ( 0.0) | 7 ( 35.0) | ||
| 4 | 2 ( 8.7) | 5 ( 25.0) | ||
| noinfo | 0 ( 0.0) | 2 ( 10.0) | ||
| MG_FAR_piglet_addfeedage (%) | 0.270 | |||
| <3 | 0 ( 0.0) | 1 ( 5.0) | ||
| <7 | 14 (60.9) | 8 ( 40.0) | ||
| >20 | 1 ( 4.3) | 0 ( 0.0) | ||
| 7-14 | 8 (34.8) | 11 ( 55.0) | ||
| MG_FAR_bed_yn = 1 (%) | 17 (73.9) | 16 ( 80.0) | 0.913 | |
| MG_FAR_bed12345_NO (%) | 0.374 | |||
| 0 | 6 (26.1) | 4 ( 20.0) | ||
| 1 | 2 ( 8.7) | 1 ( 5.0) | ||
| 2 | 4 (17.4) | 7 ( 35.0) | ||
| 4 | 1 ( 4.3) | 0 ( 0.0) | ||
| 5 | 1 ( 4.3) | 0 ( 0.0) | ||
| 12 | 6 (26.1) | 4 ( 20.0) | ||
| 14 | 2 ( 8.7) | 0 ( 0.0) | ||
| 15 | 1 ( 4.3) | 0 ( 0.0) | ||
| 24 | 0 ( 0.0) | 1 ( 5.0) | ||
| 25 | 0 ( 0.0) | 2 ( 10.0) | ||
| 245 | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_FAR_bedamount (%) | 0.150 | |||
| 0 | 0 ( 0.0) | 1 ( 5.0) | ||
| 1 | 4 (17.4) | 1 ( 5.0) | ||
| 2 | 3 (13.0) | 8 ( 40.0) | ||
| 3 | 9 (39.1) | 7 ( 35.0) | ||
| 4 | 7 (30.4) | 3 ( 15.0) | ||
| MG_FAR_root_yn = 1 (%) | 18 (78.3) | 17 ( 85.0) | 0.862 | |
| MG_FAR_toy = 1 (%) | 11 (47.8) | 12 ( 60.0) | 0.623 | |
| MG_FAR_rootamount (%) | 0.263 | |||
| 0 | 2 ( 8.7) | 3 ( 15.0) | ||
| 1 | 5 (21.7) | 1 ( 5.0) | ||
| 2 | 16 (69.6) | 16 ( 80.0) | ||
| MG_FAR_dirtmed = 2 (%) | 7 (33.3) | 8 ( 47.1) | 0.598 | |
| MG_FAR_diranimmed = 2 (%) | 9 (45.0) | 8 ( 44.4) | 1.000 | |
| MG_FAR_toytoinen_MIKA_NO (%) | 0.525 | |||
| 2 ( 8.7) | 0 ( 0.0) | |||
| 0 | 1 ( 4.3) | 1 ( 5.0) | ||
| 1 | 6 (26.1) | 3 ( 15.0) | ||
| 2 | 11 (47.8) | 14 ( 70.0) | ||
| 3 | 2 ( 8.7) | 2 ( 10.0) | ||
| noinfo | 1 ( 4.3) | 0 ( 0.0) | ||
| OUT_SOW_mort_dic = 1 (%) | 0 ( 0.0) | 20 (100.0) | <0.001 | |
| OUT_SOW_totrem_dic = 1 (%) | 7 (30.4) | 15 ( 75.0) | 0.009 | |
| OUT_SOW_cull_dic = 1 (%) | 11 (47.8) | 10 ( 50.0) | 1.000 |
Yhteenveto poistojen mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')table2%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 22 | 21 | ||
| MG_FAR_ToFarunitNUM_NO (mean (sd)) | 2.77 (1.38) | 3.38 (1.50) | 0.173 | |
| MG_FAR_NestmatdaysNUM_NO (mean (sd)) | 4.55 (2.09) | 4.24 (2.41) | 0.656 | |
| MG_FAR_piglet_rem_ageNUM_NO (mean (sd)) | 3.64 (1.62) | 3.24 (2.10) | 0.488 | |
| MG_FAR_pigletremaount_NUM_NO (mean (sd)) | 11.64 (6.24) | 12.57 (7.40) | 0.656 | |
| MG_FAR_toynum_NO (mean (sd)) | 3.85 (1.95) | 4.27 (1.67) | 0.544 | |
| MG_FAR_dirt_NUM_NO (mean (sd)) | 2.16 (1.26) | 2.58 (1.61) | 0.375 | |
| MG_FAR_diranim_NUM_NO (mean (sd)) | 2.63 (1.54) | 3.21 (2.15) | 0.346 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 8.73 (5.55) | 8.76 (4.73) | 0.983 | |
| OUT_SOW_totremproNUM (mean (sd)) | 7.77 (5.46) | 18.62 (6.82) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 7.18 (3.57) | 20.00 (4.27) | <0.001 | |
| MG_FAR_ind_0no_1rout_2sometimes (%) | 0.164 | |||
| 0 | 11 (50.0) | 6 ( 28.6) | ||
| 1 | 0 ( 0.0) | 2 ( 9.5) | ||
| 2 | 11 (50.0) | 13 ( 61.9) | ||
| MG_FAR_nestmatamount (%) | 0.038 | |||
| 0 | 2 ( 9.1) | 3 ( 14.3) | ||
| 1 | 4 (18.2) | 0 ( 0.0) | ||
| 2 | 14 (63.6) | 10 ( 47.6) | ||
| 3 | 2 ( 9.1) | 8 ( 38.1) | ||
| MG_FAR_nestmat_NO (%) | 0.210 | |||
| _CUT | 2 ( 9.1) | 0 ( 0.0) | ||
| _NWS | 2 ( 9.1) | 3 ( 14.3) | ||
| 0 | 2 ( 9.1) | 3 ( 14.3) | ||
| heiina | 1 ( 4.5) | 0 ( 0.0) | ||
| heina | 0 ( 0.0) | 1 ( 4.8) | ||
| heina puruturve | 0 ( 0.0) | 1 ( 4.8) | ||
| heina turve | 1 ( 4.5) | 0 ( 0.0) | ||
| no | 0 ( 0.0) | 1 ( 4.8) | ||
| STR | 11 (50.0) | 6 ( 28.6) | ||
| STR_CUT | 1 ( 4.5) | 6 ( 28.6) | ||
| STR_CUT_NWS | 1 ( 4.5) | 0 ( 0.0) | ||
| STR_NWS | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_FAR_ox_0_13_46_7 (%) | 0.652 | |||
| 0 | 1 ( 4.5) | 0 ( 0.0) | ||
| 1 | 9 (40.9) | 9 ( 45.0) | ||
| 2 | 4 (18.2) | 2 ( 10.0) | ||
| 3 | 8 (36.4) | 9 ( 45.0) | ||
| MG_FAR_obstex_preox (%) | 0.541 | |||
| 0 | 13 (59.1) | 13 ( 61.9) | ||
| 1 | 9 (40.9) | 7 ( 33.3) | ||
| noinfo | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_FAR_far_assist_CAT (%) | 0.289 | |||
| <6 | 6 (27.3) | 5 ( 23.8) | ||
| 10 | 0 ( 0.0) | 1 ( 4.8) | ||
| 15 | 1 ( 4.5) | 0 ( 0.0) | ||
| 20-50 | 4 (18.2) | 4 ( 19.0) | ||
| 50 | 4 (18.2) | 0 ( 0.0) | ||
| 6-20 | 6 (27.3) | 8 ( 38.1) | ||
| noinfo | 1 ( 4.5) | 3 ( 14.3) | ||
| MG_FAR_farassist_MAY_NO (%) | 0.463 | |||
| 0 ( 0.0) | 2 ( 9.5) | |||
| _GLO_LUBR | 9 (40.9) | 6 ( 28.6) | ||
| _HANDWASH_GLO_LUBR | 0 ( 0.0) | 1 ( 4.8) | ||
| GLO_LUBR | 0 ( 0.0) | 1 ( 4.8) | ||
| WASH_GLO_LUBR | 6 (27.3) | 7 ( 33.3) | ||
| WASH_HANDWASH_GLO_LUBR | 6 (27.3) | 3 ( 14.3) | ||
| WASH_HANDWASH_LUBR | 1 ( 4.5) | 1 ( 4.8) | ||
| MG_FAR_piglet_rem_amountCAT (%) | 0.088 | |||
| 0 | 0 ( 0.0) | 1 ( 4.8) | ||
| 1 | 10 (45.5) | 5 ( 23.8) | ||
| 2 | 6 (27.3) | 5 ( 23.8) | ||
| 3 | 5 (22.7) | 2 ( 9.5) | ||
| 4 | 1 ( 4.5) | 6 ( 28.6) | ||
| noinfo | 0 ( 0.0) | 2 ( 9.5) | ||
| MG_FAR_piglet_addfeedage (%) | 0.530 | |||
| <3 | 0 ( 0.0) | 1 ( 4.8) | ||
| <7 | 12 (54.5) | 10 ( 47.6) | ||
| >20 | 1 ( 4.5) | 0 ( 0.0) | ||
| 7-14 | 9 (40.9) | 10 ( 47.6) | ||
| MG_FAR_bed_yn = 1 (%) | 18 (81.8) | 15 ( 71.4) | 0.656 | |
| MG_FAR_bed12345_NO (%) | 0.798 | |||
| 0 | 4 (18.2) | 6 ( 28.6) | ||
| 1 | 2 ( 9.1) | 1 ( 4.8) | ||
| 2 | 5 (22.7) | 6 ( 28.6) | ||
| 4 | 0 ( 0.0) | 1 ( 4.8) | ||
| 5 | 1 ( 4.5) | 0 ( 0.0) | ||
| 12 | 6 (27.3) | 4 ( 19.0) | ||
| 14 | 1 ( 4.5) | 1 ( 4.8) | ||
| 15 | 1 ( 4.5) | 0 ( 0.0) | ||
| 24 | 0 ( 0.0) | 1 ( 4.8) | ||
| 25 | 1 ( 4.5) | 1 ( 4.8) | ||
| 245 | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_FAR_bedamount (%) | 0.538 | |||
| 0 | 0 ( 0.0) | 1 ( 4.8) | ||
| 1 | 4 (18.2) | 1 ( 4.8) | ||
| 2 | 6 (27.3) | 5 ( 23.8) | ||
| 3 | 7 (31.8) | 9 ( 42.9) | ||
| 4 | 5 (22.7) | 5 ( 23.8) | ||
| MG_FAR_root_yn = 1 (%) | 19 (86.4) | 16 ( 76.2) | 0.642 | |
| MG_FAR_toy = 1 (%) | 10 (45.5) | 13 ( 61.9) | 0.438 | |
| MG_FAR_rootamount (%) | 0.277 | |||
| 0 | 1 ( 4.5) | 4 ( 19.0) | ||
| 1 | 4 (18.2) | 2 ( 9.5) | ||
| 2 | 17 (77.3) | 15 ( 71.4) | ||
| MG_FAR_dirtmed = 2 (%) | 7 (36.8) | 8 ( 42.1) | 1.000 | |
| MG_FAR_diranimmed = 2 (%) | 7 (36.8) | 10 ( 52.6) | 0.514 | |
| MG_FAR_toytoinen_MIKA_NO (%) | 0.801 | |||
| 1 ( 4.5) | 1 ( 4.8) | |||
| 0 | 1 ( 4.5) | 1 ( 4.8) | ||
| 1 | 3 (13.6) | 6 ( 28.6) | ||
| 2 | 14 (63.6) | 11 ( 52.4) | ||
| 3 | 2 ( 9.1) | 2 ( 9.5) | ||
| noinfo | 1 ( 4.5) | 0 ( 0.0) | ||
| OUT_SOW_mort_dic = 1 (%) | 10 (45.5) | 10 ( 47.6) | 1.000 | |
| OUT_SOW_totrem_dic = 1 (%) | 5 (22.7) | 17 ( 81.0) | <0.001 | |
| OUT_SOW_cull_dic = 1 (%) | 0 ( 0.0) | 21 (100.0) | <0.001 |
Yhteenveto joku hylkays mukaan
res_mca = MCA(medmca, quanti.sup = c(18,19),quali.sup=c(16:17), graph = FALSE)summary(res_mca)##
## Call:
## MCA(X = medmca, quanti.sup = c(18, 19), quali.sup = c(16:17),
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6
## Variance 0.326 0.271 0.212 0.183 0.157 0.147
## % of var. 12.520 10.406 8.173 7.044 6.057 5.661
## Cumulative % of var. 12.520 22.926 31.099 38.142 44.199 49.860
## Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Dim.12
## Variance 0.138 0.126 0.120 0.103 0.097 0.087
## % of var. 5.316 4.862 4.624 3.977 3.726 3.361
## Cumulative % of var. 55.176 60.038 64.662 68.639 72.365 75.726
## Dim.13 Dim.14 Dim.15 Dim.16 Dim.17 Dim.18
## Variance 0.081 0.073 0.064 0.056 0.055 0.048
## % of var. 3.106 2.798 2.467 2.147 2.117 1.848
## Cumulative % of var. 78.832 81.630 84.096 86.243 88.360 90.208
## Dim.19 Dim.20 Dim.21 Dim.22 Dim.23 Dim.24
## Variance 0.042 0.036 0.032 0.030 0.023 0.020
## % of var. 1.612 1.385 1.234 1.149 0.867 0.760
## Cumulative % of var. 91.820 93.205 94.439 95.588 96.456 97.216
## Dim.25 Dim.26 Dim.27 Dim.28 Dim.29 Dim.30
## Variance 0.017 0.015 0.011 0.010 0.007 0.004
## % of var. 0.638 0.577 0.429 0.367 0.273 0.168
## Cumulative % of var. 97.854 98.431 98.860 99.228 99.501 99.668
## Dim.31 Dim.32 Dim.33 Dim.34 Dim.35 Dim.36
## Variance 0.004 0.003 0.002 0.001 0.000 0.000
## % of var. 0.153 0.098 0.059 0.021 0.000 0.000
## Cumulative % of var. 99.822 99.920 99.979 100.000 100.000 100.000
## Dim.37 Dim.38 Dim.39
## Variance 0.000 0.000 0.000
## % of var. 0.000 0.000 0.000
## Cumulative % of var. 100.000 100.000 100.000
##
## Individuals (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr
## 1 | 0.404 1.165 0.057 | -0.969 8.069
## 2 | -0.273 0.533 0.056 | 0.221 0.420
## 3 | -0.290 0.601 0.080 | 0.099 0.084
## 4 | -0.131 0.123 0.011 | 0.065 0.036
## 5 | 0.595 2.527 0.153 | -0.227 0.444
## 6 | -0.159 0.181 0.004 | 0.098 0.083
## 7 | 0.165 0.195 0.007 | 0.053 0.024
## 8 | -0.253 0.458 0.022 | 0.320 0.881
## 9 | -0.296 0.627 0.055 | 0.287 0.707
## 10 | -0.016 0.002 0.000 | 0.001 0.000
## cos2 Dim.3 ctr cos2
## 1 0.330 | 0.089 0.087 0.003 |
## 2 0.037 | -0.020 0.004 0.000 |
## 3 0.009 | -0.265 0.767 0.066 |
## 4 0.003 | -0.453 2.242 0.134 |
## 5 0.022 | 0.099 0.108 0.004 |
## 6 0.001 | -0.631 4.356 0.061 |
## 7 0.001 | -0.268 0.788 0.018 |
## 8 0.035 | -0.556 3.382 0.105 |
## 9 0.052 | -0.107 0.125 0.007 |
## 10 0.000 | -0.401 1.764 0.053 |
##
## Categories (the 10 first)
## Dim.1 ctr cos2 v.test Dim.2
## MG_FAR_ind_0no_1rout_2sometimes_0 | -0.575 2.675 0.216 -3.012 | 0.311
## MG_FAR_ind_0no_1rout_2sometimes_1 | 1.938 3.577 0.183 2.774 | -1.396
## MG_FAR_ind_0no_1rout_2sometimes_2 | 0.246 0.690 0.076 1.789 | -0.104
## MG_FAR_nestmatamount_0 | 1.236 3.638 0.201 2.905 | -1.159
## MG_FAR_nestmatamount_1 | -0.791 1.192 0.064 -1.642 | 0.566
## MG_FAR_nestmatamount_2 | -0.431 2.121 0.234 -3.138 | 0.213
## MG_FAR_nestmatamount_3 | 0.732 2.555 0.163 2.613 | -0.159
## MG_FAR_ox_0_13_46_7_0 | -1.591 1.206 0.060 -1.591 | 1.484
## MG_FAR_ox_0_13_46_7_1 | -0.439 1.649 0.138 -2.412 | 0.097
## MG_FAR_ox_0_13_46_7_2 | -0.533 0.813 0.046 -1.392 | 0.383
## ctr cos2 v.test Dim.3 ctr
## MG_FAR_ind_0no_1rout_2sometimes_0 0.942 0.063 1.629 | 0.270 0.906
## MG_FAR_ind_0no_1rout_2sometimes_1 2.233 0.095 -1.998 | 1.818 4.825
## MG_FAR_ind_0no_1rout_2sometimes_2 0.148 0.014 -0.757 | -0.343 2.060
## MG_FAR_nestmatamount_0 3.852 0.177 -2.726 | 0.904 2.979
## MG_FAR_nestmatamount_1 0.734 0.033 1.174 | 0.205 0.122
## MG_FAR_nestmatamount_2 0.626 0.058 1.554 | -0.177 0.547
## MG_FAR_nestmatamount_3 0.144 0.008 -0.566 | -0.109 0.087
## MG_FAR_ox_0_13_46_7_0 1.262 0.052 1.484 | 4.374 13.957
## MG_FAR_ox_0_13_46_7_1 0.097 0.007 0.533 | -0.066 0.057
## MG_FAR_ox_0_13_46_7_2 0.504 0.024 0.999 | -0.284 0.352
## cos2 v.test
## MG_FAR_ind_0no_1rout_2sometimes_0 0.048 1.416 |
## MG_FAR_ind_0no_1rout_2sometimes_1 0.161 2.603 |
## MG_FAR_ind_0no_1rout_2sometimes_2 0.149 -2.498 |
## MG_FAR_nestmatamount_0 0.107 2.124 |
## MG_FAR_nestmatamount_1 0.004 0.425 |
## MG_FAR_nestmatamount_2 0.039 -1.288 |
## MG_FAR_nestmatamount_3 0.004 -0.391 |
## MG_FAR_ox_0_13_46_7_0 0.455 4.374 |
## MG_FAR_ox_0_13_46_7_1 0.003 -0.363 |
## MG_FAR_ox_0_13_46_7_2 0.013 -0.740 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## MG_FAR_ind_0no_1rout_2sometimes | 0.339 0.135 0.248 |
## MG_FAR_nestmatamount | 0.464 0.217 0.119 |
## MG_FAR_ox_0_13_46_7 | 0.710 0.618 0.461 |
## MG_FAR_obstex_preox | 0.452 0.428 0.032 |
## MG_FAR_far_assist_CAT | 0.583 0.142 0.344 |
## MG_FAR_piglet_rem_amountCAT | 0.647 0.242 0.177 |
## MG_FAR_piglet_addfeedage | 0.502 0.487 0.472 |
## MG_FAR_bed_yn | 0.067 0.365 0.021 |
## MG_FAR_bedamount | 0.314 0.370 0.440 |
## MG_FAR_root_yn | 0.243 0.381 0.089 |
##
## Supplementary categories
## Dim.1 cos2 v.test Dim.2 cos2
## OUT_SOW_totrem_dic_0 | -0.290 0.080 -1.836 | -0.052 0.003
## OUT_SOW_totrem_dic_1 | 0.277 0.080 1.836 | 0.050 0.003
## OUT_SOW_cull_dic_0 | -0.309 0.100 -2.050 | 0.055 0.003
## OUT_SOW_cull_dic_1 | 0.324 0.100 2.050 | -0.057 0.003
## v.test Dim.3 cos2 v.test
## OUT_SOW_totrem_dic_0 -0.329 | 0.139 0.018 0.880 |
## OUT_SOW_totrem_dic_1 0.329 | -0.133 0.018 -0.880 |
## OUT_SOW_cull_dic_0 0.363 | -0.010 0.000 -0.067 |
## OUT_SOW_cull_dic_1 -0.363 | 0.011 0.000 0.067 |
##
## Supplementary categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## OUT_SOW_totrem_dic | 0.080 0.003 0.018 |
## OUT_SOW_cull_dic | 0.100 0.003 0.000 |
##
## Supplementary continuous variables
## Dim.1 Dim.2 Dim.3
## OUT_SOW_mort_proNUM | 0.234 | -0.037 | -0.135 |
## OUT_SOW_cullproNUM | 0.332 | 0.073 | -0.129 |To visualize the percentage of inertia explained by each MCA dimension:
eig.val <- res_mca$eig
barplot(eig.val[, 2],
names.arg = 1:nrow(eig.val),
main = "Variances Explained by Dimensions (%)",
xlab = "Principal Dimensions",
ylab = "Percentage of variances",
col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2],
type = "b", pch = 19, col = "red")
res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
var_sup = TRUE, var_lab_min_contrib = 0,
col_var = "Variable", symbol_var = "Type",
size_var = NULL, size_range = c(10, 300),
labels_size = 10, point_size = 56,
transitions = TRUE, labels_positions = NULL)res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
lab_var = NULL, , ind_lab_min_contrib = 0,
col_var = NULL, labels_size = 9,
point_opacity = 0.5, opacity_var = NULL, point_size = 64,
ellipses = FALSE, transitions = TRUE, labels_positions = NULL)fviz_mca_var(res_mca, choice = "quanti.sup",
ggtheme = theme_minimal())
## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```
##
## ```
## drawn <-
## c("13", "39", "43", "1", "24", "21", "18", "42", "31", "16")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
##
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
##
##
## The cluster 1 is made of individuals such as*. This group is characterized by16* and *16*. :
##
## - high frequency for the factors *MG_FAR_root_yn=MG_FAR_root_yn_1*, *MG_FAR_nestmatamount=MG_FAR_nestmatamount_2*, *MG_FAR_bed_yn=MG_FAR_bed_yn_1*, *MG_FAR_bedamount=MG_FAR_bedamount_2*, *MG_FAR_far_assist_CAT=MG_FAR_far_assist_CAT_<6*, *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_1* and *MG_FAR_ind_0no_1rout_2sometimes=MG_FAR_ind_0no_1rout_2sometimes_0* (factors are sorted from the most common).
## - low frequency for the factors *MG_FAR_root_yn=MG_FAR_root_yn_0*, *MG_FAR_bed_yn=MG_FAR_bed_yn_0*, *MG_FAR_nestmatamount=MG_FAR_nestmatamount_0*, *MG_FAR_rootamount=MG_FAR_rootamount_0*, *MG_FAR_nestmatamount=MG_FAR_nestmatamount_3*, *MG_FAR_bedamount=MG_FAR_bedamount_4*, *MG_FAR_far_assist_CAT=MG_FAR_far_assist_CAT_noinfo* and *MG_FAR_ox_0_13_46_7=MG_FAR_ox_0_13_46_7_3* (factors are sorted from the rarest).
##
## The cluster 2 is made of individuals such as*. This group is characterized by1* and *1*. :
##
## - high frequency for the factors *MG_FAR_root_yn=MG_FAR_root_yn_0*, *MG_FAR_bed_yn=MG_FAR_bed_yn_0*, *MG_FAR_rootamount=MG_FAR_rootamount_0*, *MG_FAR_nestmatamount=MG_FAR_nestmatamount_0*, *MG_FAR_bedamount=MG_FAR_bedamount_4*, *MG_FAR_ox_0_13_46_7=MG_FAR_ox_0_13_46_7_3*, *MG_FAR_nestmatamount=MG_FAR_nestmatamount_3*, *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_2* and *MG_FAR_diranimmed=MG_FAR_diranimmed_1* (factors are sorted from the most common).
## - low frequency for the factors *MG_FAR_root_yn=MG_FAR_root_yn_1*, *MG_FAR_nestmatamount=MG_FAR_nestmatamount_2*, *MG_FAR_bed_yn=MG_FAR_bed_yn_1*, *MG_FAR_far_assist_CAT=MG_FAR_far_assist_CAT_<6*, *MG_FAR_bedamount=MG_FAR_bedamount_2* and *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_1* (factors are sorted from the rarest).
##
## The 1st cluster is made of individuals such as *13*. This group is characterized by :
##
## - high frequency for the factors *MG_FAR_piglet_addfeedage=<3*, *MG_FAR_obstex_preox=MG_FAR_obstex_preox_noinfo*, *MG_FAR_ox_0_13_46_7=MG_FAR_ox_0_13_46_7_Not Assigned* and *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_noinfo* (factors are sorted from the most common).## **Results for the Hierarchical Clustering on Principal Components**
## name
## 1 "$data.clust"
## 2 "$desc.var"
## 3 "$desc.var$test.chi2"
## 4 "$desc.axes$category"
## 5 "$desc.axes"
## 6 "$desc.axes$quanti.var"
## 7 "$desc.axes$quanti"
## 8 "$desc.ind"
## 9 "$desc.ind$para"
## 10 "$desc.ind$dist"
## 11 "$call"
## 12 "$call$t"
## description
## 1 "dataset with the cluster of the individuals"
## 2 "description of the clusters by the variables"
## 3 "description of the cluster var. by the categorical var."
## 4 "description of the clusters by the categories."
## 5 "description of the clusters by the dimensions"
## 6 "description of the cluster var. by the axes"
## 7 "description of the clusters by the axes"
## 8 "description of the clusters by the individuals"
## 9 "parangons of each clusters"
## 10 "specific individuals"
## 11 "summary statistics"
## 12 "description of the tree"Management
# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="manag.csv", header=TRUE)glimpse(med)## Observations: 43
## Variables: 85
## $ MG_feedtimes_preg <int> 2, 2, 1, 2, NA, 2, 2, 2,...
## $ MG_feedtimes_far <int> 3, 3, 3, 4, NA, 3, 4, 3,...
## $ MG_feed_liq_solid <fctr> liq, liq, liq, liq, , l...
## $ MG_owngilts <int> 90, 100, 95, 0, 0, 100, ...
## $ MG_owngilts.1 <int> 1, 1, 1, 0, 0, 1, 1, 0, ...
## $ MG_BR_giltpurchage_NUM_NO <int> 4, 0, 0, 7, 3, 0, 0, 5, ...
## $ MG_BR_giltchangebeforeins_NO <int> 0, 0, 1, 0, 1, 0, 1, 0, ...
## $ MG_BR_giltflush_NO <fctr> 0, 1, 0, 0, 1, 0, 0, 1,...
## $ MG_BR_giltboarstart_NO <fctr> 7, 6, 7,5, 7,5, 7, 7,5,...
## $ MG_BR_giltinsage_NO <fctr> 8, 7, 8, 7,5, 8, 9,5, 8...
## $ MG_BR_heatgroup_NO <fctr> 0, 1, 1, 1, 1, 1, 1, 1,...
## $ MG_BR_heatdetec_startNUM_NO <fctr> 0, 0, 5, 0, 1, 0, 3, 3,...
## $ MG_BR_heatmarkback_NO <fctr> 1, 0, 1, 1, 1, 1, 1, 0,...
## $ MG_BR_artinspro_050_5099_100 <int> 1, 1, 2, 2, 2, 1, 1, 2, ...
## $ MG_BR_farmsemenNUM_NO <int> 0, 0, 95, 0, 50, 0, 0, 0...
## $ MG_BR_insonceNUM_NO <fctr> 0, 8, 0, 10, 0, 10, 80,...
## $ MG_BR_once_012 <int> 0, 1, 0, 1, 0, 1, 2, 1, ...
## $ MG_BR_instriple_NO <fctr> 1, 2, 10, 10, 15, 0, 0,...
## $ MG_BR_triple_012 <fctr> 1, 1, 1, 1, 2, 0, 0, 1,...
## $ MG_aveins <fctr> 2, 2, 2, 2, 2, 2, 2, 2,...
## $ MG_BR_nopregus <fctr> 1, 1, 2, 1, 2, 0, 0, 1,...
## $ MG_FAR_ToFarunitNUM_NO <int> 6, 5, 4, 7, 7, 7, 3, 5, ...
## $ MG_FAR_ind_0no_1rout_2sometimes <int> 2, 0, 2, 2, 2, 2, 2, 0, ...
## $ MG_FAR_NestmatdaysNUM_NO <int> 6, 1, 4, 7, 2, 0, 3, 2, ...
## $ MG_FAR_nestmatamount <int> 3, 2, 2, 2, 3, 2, 3, 1, ...
## $ MG_FAR_nestmat_NO <fctr> STR, STR, STR_CUT, heii...
## $ MG_FAR_ox_0_13_46_7 <int> 3, 1, 1, 1, 3, 3, 3, 2, ...
## $ MG_FAR_obstex_preox <fctr> 1, 0, 0, 1, 0, 0, 0, 1,...
## $ MG_FAR_far_assist_CAT <fctr> 50, 20-50, <6, 20-50, n...
## $ MG_FAR_farassist_MAY_NO <fctr> WASH_GLO_LUBR, WASH_HAN...
## $ MG_FAR_piglet_rem_ageNUM_NO <fctr> 0,5, 1, 0,5, 0,5, 1, no...
## $ MG_FAR_piglet_rem_amountCAT <fctr> 1, 1, 1, 3, 4, 0, 1, 3,...
## $ MG_FAR_pigletremaount_NUM_NO <fctr> 9, 5, 4, 25, 50, 0, 10,...
## $ MG_FAR_piglet_addfeedage <fctr> 7-14, 7-14, <7, <7, <7,...
## $ MG_ind_feed <int> 1, 1, 1, 1, 1, 1, 1, 0, ...
## $ MG_BR_bedtype_NO <int> 0, 1, 12, 0, 0, 14, 1, 1...
## $ MG_BR_bedny <int> 0, 1, 1, 0, 0, 1, 1, 1, ...
## $ MG_BR_amount <int> 4, 2, 1, 0, 0, 1, 3, 2, ...
## $ MG_BR_rootny <int> 0, 1, 1, 0, 0, 1, 0, 1, ...
## $ MG_BR_toyny <fctr> 0, 0, 0, 0, 1, 0, 0, 1,...
## $ MG_sickpen_yn <int> 0, 1, 1, 1, 1, 0, 1, 1, ...
## $ MG_BR_dirt_NUM_NO <int> 30, 0, 0, 20, 40, 0, 20,...
## $ MG_BR_animdirtmed <int> 2, 1, 1, 2, 2, 1, 2, 2, ...
## $ MG_BR_feedtype <int> 4, 1, 25, 4, 4, 4, 4, 4,...
## $ MG_BR_feedclean <fctr> 0, 0, 0, 0, 1, 0, 0, 1,...
## $ MG_BR_calm <int> 2, 1, 1, 1, 1, 1, 2, 1, ...
## $ MG_BR_dirtanim_NUM_NO <int> 20, 0, 10, 10, NA, 20, 3...
## $ MG_BR_dirtanimmed <int> 2, 1, 1, 1, NA, 2, 2, 2,...
## $ MG_BR_ster <int> 1, 0, 0, 0, 0, 0, 0, 0, ...
## $ MG_PR_earlyHAR_kaNUM <fctr> 0,9, 0,15, 0, 0,2, 1, 0...
## $ MG_PR_type <int> 2, 1, 12, 13, 2, 1, 2, 1...
## $ MG_PR_rootyn <int> 1, 1, 1, 0, 1, 1, 1, 1, ...
## $ MG_PR_toyyn <int> 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ MG_PR_toy <int> 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ MG_rootamount <int> 2, 2, 1, 0, 1, 1, 2, 1, ...
## $ MG_PR_kuivaliete <int> 2, 1, 1, 2, 2, 1, 2, 1, ...
## $ MG_PR_ruok_0nonlock_1lock <int> 0, 0, 0, 1, 0, 1, 1, 0, ...
## $ MG_PR_feedtype <int> 3, 1, 25, 4, 5, 4, 4, 5,...
## $ MG_PR_calm <int> 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ MG_PR_dirt_NUM_NO <int> 20, 0, 10, 20, 20, 20, 2...
## $ MG_PR_animdirtmed <int> 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ MG_PR_ster <int> 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ MG_PR_late_HAR_kaNUM_NO <fctr> 0,9, 0,15, 0, 0,1, , , ...
## $ MG_FAR_bed_yn <int> 0, 1, 1, 1, 0, 1, 1, 1, ...
## $ MG_FAR_bed12345_NO <int> 0, 14, 12, 25, 0, 1, 1, ...
## $ MG_FAR_bedamount <int> 4, 3, 3, 3, 4, 2, 3, 2, ...
## $ MG_FAR_root_yn <int> 0, 1, 1, 1, 1, 1, 1, 1, ...
## $ MG_FAR_toy <int> 0, 0, 0, 0, 1, 0, 0, 0, ...
## $ MG_FAR_toynum_NO <int> 0, NA, NA, 0, 4, NA, NA,...
## $ MG_FAR_rootamount <int> 0, 2, 2, 2, 2, 2, 2, 2, ...
## $ MG_FAR_dirt_NUM_NO <int> 10, 30, 0, 20, 20, 0, 0,...
## $ MG_FAR_dirtmed <int> 1, 2, 1, 2, 2, 1, 1, NA,...
## $ MG_FAR_diranim_NUM_NO <int> 10, 20, 0, 10, 15, 20, 3...
## $ MG_FAR_diranimmed <int> 1, 2, 1, 1, 2, 2, 2, 1, ...
## $ MG_BR_sowsperboar_NUM_NO <fctr> 150, 37, 75, 92, 525, 3...
## $ MG_FAR_toytoinen_MIKA_NO <fctr> 0, 2, 2, 2, 2, , 2, 2, ...
## $ MG_SOWSperworkeredit_NUM57_113_147_NO <int> 158, 20, 113, 126, 340, ...
## $ MG_SOWSperworkeredit_57_113_147_ <int> 4, 1, 2, 3, 4, 1, 2, 3, ...
## $ MG_SOWSperworker_NUM_NO <int> 150, NA, 100, 138, 350, ...
## $ OUT_SOW_mort_proNUM <int> 5, 5, 8, 27, 10, 0, 17, ...
## $ OUT_SOW_mort_dic <int> 0, 0, 0, 1, 1, 0, 1, 1, ...
## $ OUT_SOW_totremproNUM <int> 34, 38, 53, 57, 65, 64, ...
## $ OUT_SOW_totrem_dic <int> 0, 0, 1, 1, 1, 1, 1, 0, ...
## $ OUT_SOW_cullproNUM <int> 29, 33, 45, 30, 55, 64, ...
## $ OUT_SOW_cull_dic <int> 0, 0, 1, 0, 1, 1, 0, 0, ...med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM ## [1] 29 33 45 30 55 64 30 31 24 28 29 36 70 27 42 22 40
## [18] 29 38 38 27 30 44 45 34 45 41 35 49 32 39 80 14 24
## [35] 35 56 39 50 42 35 33 107 42
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107colnames(med)=="OUT_SOW_cullproNUM"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [78] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSEcolnames(med)=="OUT_SOWmortpro"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [78] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEmed$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))
X<-medmca
colnames(X)[ apply(X, 2, anyNA) ]## [1] "MG_feedtimes_preg" "MG_feedtimes_far" "MG_BR_once_012"
## [4] "MG_FAR_ox_0_13_46_7" "MG_BR_animdirtmed" "MG_BR_dirtanimmed"
## [7] "MG_PR_animdirtmed" "MG_FAR_dirtmed" "MG_FAR_diranimmed"for (i in 1:ncol(X)) {
levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}
X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)
medmca<-X #colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="lightblue") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+ scale_fill_manual("key")
library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")
library(tableone)
KreateTableOne = function(x, ...){
t1 = tableone::CreateTableOne(data=x, ...)
t2 = print(t1, quote=TRUE)
rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
return(t2)
}Yhteenveto kuolleisuuden mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')table1%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 23 | 20 | ||
| MG_BR_giltpurchage_NUM_NO (mean (sd)) | 3.00 (2.63) | 2.40 (2.26) | 0.430 | |
| MG_BR_heatdetec_startNUM_NO (mean (sd)) | 3.35 (2.44) | 3.55 (2.37) | 0.785 | |
| MG_BR_farmsemenNUM_NO (mean (sd)) | 1.96 (2.06) | 2.00 (1.97) | 0.944 | |
| MG_BR_insonceNUM_NO (mean (sd)) | 4.04 (3.75) | 6.15 (3.99) | 0.082 | |
| MG_FAR_ToFarunitNUM_NO (mean (sd)) | 3.04 (1.49) | 3.10 (1.45) | 0.901 | |
| MG_FAR_NestmatdaysNUM_NO (mean (sd)) | 4.78 (2.35) | 3.95 (2.04) | 0.226 | |
| MG_FAR_piglet_rem_ageNUM_NO (mean (sd)) | 3.70 (1.84) | 3.15 (1.87) | 0.342 | |
| MG_FAR_pigletremaount_NUM_NO (mean (sd)) | 11.26 (7.15) | 13.05 (6.34) | 0.393 | |
| MG_BR_dirt_NUM_NO (mean (sd)) | 2.27 (1.52) | 2.59 (1.54) | 0.527 | |
| MG_BR_dirtanim_NUM_NO (mean (sd)) | 2.52 (1.54) | 2.79 (1.23) | 0.552 | |
| MG_PR_dirt_NUM_NO (mean (sd)) | 3.00 (1.95) | 4.26 (2.38) | 0.066 | |
| MG_PR_late_HAR_kaNUM_NO (mean (sd)) | 4.87 (4.70) | 5.95 (5.57) | 0.494 | |
| MG_FAR_toynum_NO (mean (sd)) | 4.31 (1.89) | 3.87 (1.73) | 0.524 | |
| MG_FAR_dirt_NUM_NO (mean (sd)) | 2.19 (1.25) | 2.59 (1.66) | 0.405 | |
| MG_FAR_diranim_NUM_NO (mean (sd)) | 2.80 (1.67) | 3.06 (2.10) | 0.679 | |
| MG_BR_sowsperboar_NUM_NO (mean (sd)) | 19.65 (10.08) | 17.55 (11.15) | 0.520 | |
| MG_SOWSperworker_NUM_NO (mean (sd)) | 16.27 (9.73) | 21.11 (10.05) | 0.126 | |
| MG_PR_earlyHAR_kaNUM (mean (sd)) | 7.91 (4.73) | 10.15 (5.41) | 0.156 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 4.74 (2.12) | 13.35 (3.27) | <0.001 | |
| OUT_SOW_totremproNUM (mean (sd)) | 8.91 (5.80) | 17.85 (8.03) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 11.78 (6.69) | 15.35 (8.20) | 0.124 | |
| MG_feedtimes_preg (%) | 0.617 | |||
| 1 | 1 ( 4.5) | 1 ( 5.3) | ||
| 2 | 17 ( 77.3) | 17 ( 89.5) | ||
| 3 | 3 ( 13.6) | 1 ( 5.3) | ||
| 4 | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_feedtimes_far (%) | 0.099 | |||
| 2 | 4 ( 18.2) | 0 ( 0.0) | ||
| 3 | 13 ( 59.1) | 16 ( 84.2) | ||
| 4 | 5 ( 22.7) | 3 ( 15.8) | ||
| MG_feed_liq_solid (%) | 0.664 | |||
| 2 ( 8.7) | 1 ( 5.0) | |||
| liq | 12 ( 52.2) | 12 ( 60.0) | ||
| liqsol | 1 ( 4.3) | 1 ( 5.0) | ||
| sol | 6 ( 26.1) | 5 ( 25.0) | ||
| solid | 2 ( 8.7) | 0 ( 0.0) | ||
| solliq | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_owngilts (%) | 0.895 | |||
| 0 | 8 ( 34.8) | 7 ( 35.0) | ||
| 50 | 0 ( 0.0) | 1 ( 5.0) | ||
| 70 | 1 ( 4.3) | 0 ( 0.0) | ||
| 80 | 1 ( 4.3) | 1 ( 5.0) | ||
| 90 | 2 ( 8.7) | 1 ( 5.0) | ||
| 95 | 1 ( 4.3) | 1 ( 5.0) | ||
| 100 | 10 ( 43.5) | 9 ( 45.0) | ||
| MG_owngilts.1 = 1 (%) | 15 ( 65.2) | 13 ( 65.0) | 1.000 | |
| MG_BR_giltchangebeforeins_NO = 1 (%) | 9 ( 39.1) | 10 ( 50.0) | 0.683 | |
| MG_BR_giltflush_NO (%) | 0.299 | |||
| 0 | 13 ( 56.5) | 10 ( 50.0) | ||
| 1 | 8 ( 34.8) | 10 ( 50.0) | ||
| noneed | 2 ( 8.7) | 0 ( 0.0) | ||
| MG_BR_giltboarstart_NO (%) | 0.158 | |||
| 0 | 4 ( 17.4) | 0 ( 0.0) | ||
| 3 | 1 ( 4.3) | 0 ( 0.0) | ||
| 4 | 0 ( 0.0) | 1 ( 5.0) | ||
| 6 | 4 ( 17.4) | 4 ( 20.0) | ||
| 6,5 | 3 ( 13.0) | 0 ( 0.0) | ||
| 7 | 6 ( 26.1) | 10 ( 50.0) | ||
| 7,5 | 4 ( 17.4) | 4 ( 20.0) | ||
| 8 | 1 ( 4.3) | 0 ( 0.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_BR_giltinsage_NO (%) | 0.139 | |||
| 0 | 3 ( 13.0) | 0 ( 0.0) | ||
| 2ndheat | 0 ( 0.0) | 1 ( 5.0) | ||
| 6 | 0 ( 0.0) | 1 ( 5.0) | ||
| 7 | 2 ( 8.7) | 0 ( 0.0) | ||
| 7,5 | 1 ( 4.3) | 1 ( 5.0) | ||
| 8 | 14 ( 60.9) | 11 ( 55.0) | ||
| 8,5 | 0 ( 0.0) | 4 ( 20.0) | ||
| 9 | 1 ( 4.3) | 0 ( 0.0) | ||
| 9,5 | 2 ( 8.7) | 1 ( 5.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_BR_heatgroup_NO (%) | 0.361 | |||
| 0 | 5 ( 21.7) | 4 ( 20.0) | ||
| 1 | 17 ( 73.9) | 14 ( 70.0) | ||
| no | 1 ( 4.3) | 0 ( 0.0) | ||
| noinfo | 0 ( 0.0) | 2 ( 10.0) | ||
| MG_BR_heatmarkback_NO (%) | 0.211 | |||
| 0 | 8 ( 34.8) | 3 ( 15.0) | ||
| 1 | 15 ( 65.2) | 16 ( 80.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_BR_artinspro_050_5099_100 (%) | 0.785 | |||
| 0 | 1 ( 4.3) | 1 ( 5.0) | ||
| 1 | 8 ( 34.8) | 5 ( 25.0) | ||
| 2 | 14 ( 60.9) | 14 ( 70.0) | ||
| MG_BR_once_012 (%) | 0.059 | |||
| 0 | 10 ( 45.5) | 5 ( 26.3) | ||
| 1 | 12 ( 54.5) | 10 ( 52.6) | ||
| 2 | 0 ( 0.0) | 4 ( 21.1) | ||
| MG_BR_instriple_NO (%) | 0.630 | |||
| 0 | 7 ( 30.4) | 4 ( 20.0) | ||
| 1 | 1 ( 4.3) | 2 ( 10.0) | ||
| 10 | 5 ( 21.7) | 6 ( 30.0) | ||
| 15 | 0 ( 0.0) | 2 ( 10.0) | ||
| 2 | 1 ( 4.3) | 0 ( 0.0) | ||
| 3 | 1 ( 4.3) | 1 ( 5.0) | ||
| 30 | 1 ( 4.3) | 0 ( 0.0) | ||
| 33 | 0 ( 0.0) | 1 ( 5.0) | ||
| 5 | 6 ( 26.1) | 3 ( 15.0) | ||
| noinfo | 1 ( 4.3) | 1 ( 5.0) | ||
| MG_BR_triple_012 (%) | 0.621 | |||
| 0 | 7 ( 30.4) | 4 ( 20.0) | ||
| 1 | 14 ( 60.9) | 12 ( 60.0) | ||
| 2 | 1 ( 4.3) | 3 ( 15.0) | ||
| noinfo | 1 ( 4.3) | 1 ( 5.0) | ||
| MG_aveins (%) | 0.299 | |||
| 1 | 0 ( 0.0) | 1 ( 5.0) | ||
| 2 | 23 (100.0) | 18 ( 90.0) | ||
| 2,1 | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_BR_nopregus (%) | 0.179 | |||
| 0 | 10 ( 43.5) | 3 ( 15.0) | ||
| 1 | 10 ( 43.5) | 12 ( 60.0) | ||
| 2 | 3 ( 13.0) | 4 ( 20.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_FAR_ind_0no_1rout_2sometimes (%) | 0.287 | |||
| 0 | 10 ( 43.5) | 7 ( 35.0) | ||
| 1 | 0 ( 0.0) | 2 ( 10.0) | ||
| 2 | 13 ( 56.5) | 11 ( 55.0) | ||
| MG_FAR_nestmatamount (%) | 0.984 | |||
| 0 | 3 ( 13.0) | 2 ( 10.0) | ||
| 1 | 2 ( 8.7) | 2 ( 10.0) | ||
| 2 | 13 ( 56.5) | 11 ( 55.0) | ||
| 3 | 5 ( 21.7) | 5 ( 25.0) | ||
| MG_FAR_nestmat_NO (%) | 0.363 | |||
| _CUT | 0 ( 0.0) | 2 ( 10.0) | ||
| _NWS | 3 ( 13.0) | 2 ( 10.0) | ||
| 0 | 3 ( 13.0) | 2 ( 10.0) | ||
| heiina | 0 ( 0.0) | 1 ( 5.0) | ||
| heina | 0 ( 0.0) | 1 ( 5.0) | ||
| heina puruturve | 0 ( 0.0) | 1 ( 5.0) | ||
| heina turve | 0 ( 0.0) | 1 ( 5.0) | ||
| no | 0 ( 0.0) | 1 ( 5.0) | ||
| STR | 11 ( 47.8) | 6 ( 30.0) | ||
| STR_CUT | 5 ( 21.7) | 2 ( 10.0) | ||
| STR_CUT_NWS | 1 ( 4.3) | 0 ( 0.0) | ||
| STR_NWS | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_FAR_ox_0_13_46_7 (%) | 0.254 | |||
| 0 | 1 ( 4.3) | 0 ( 0.0) | ||
| 1 | 10 ( 43.5) | 8 ( 42.1) | ||
| 2 | 5 ( 21.7) | 1 ( 5.3) | ||
| 3 | 7 ( 30.4) | 10 ( 52.6) | ||
| MG_FAR_obstex_preox (%) | 0.493 | |||
| 0 | 15 ( 65.2) | 11 ( 55.0) | ||
| 1 | 8 ( 34.8) | 8 ( 40.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_FAR_far_assist_CAT (%) | 0.301 | |||
| <6 | 4 ( 17.4) | 7 ( 35.0) | ||
| 10 | 1 ( 4.3) | 0 ( 0.0) | ||
| 15 | 0 ( 0.0) | 1 ( 5.0) | ||
| 20-50 | 4 ( 17.4) | 4 ( 20.0) | ||
| 50 | 3 ( 13.0) | 1 ( 5.0) | ||
| 6-20 | 10 ( 43.5) | 4 ( 20.0) | ||
| noinfo | 1 ( 4.3) | 3 ( 15.0) | ||
| MG_FAR_farassist_MAY_NO (%) | 0.350 | |||
| 0 ( 0.0) | 2 ( 10.0) | |||
| _GLO_LUBR | 8 ( 34.8) | 7 ( 35.0) | ||
| _HANDWASH_GLO_LUBR | 1 ( 4.3) | 0 ( 0.0) | ||
| GLO_LUBR | 0 ( 0.0) | 1 ( 5.0) | ||
| WASH_GLO_LUBR | 8 ( 34.8) | 5 ( 25.0) | ||
| WASH_HANDWASH_GLO_LUBR | 4 ( 17.4) | 5 ( 25.0) | ||
| WASH_HANDWASH_LUBR | 2 ( 8.7) | 0 ( 0.0) | ||
| MG_FAR_piglet_rem_amountCAT (%) | 0.001 | |||
| 0 | 1 ( 4.3) | 0 ( 0.0) | ||
| 1 | 10 ( 43.5) | 5 ( 25.0) | ||
| 2 | 10 ( 43.5) | 1 ( 5.0) | ||
| 3 | 0 ( 0.0) | 7 ( 35.0) | ||
| 4 | 2 ( 8.7) | 5 ( 25.0) | ||
| noinfo | 0 ( 0.0) | 2 ( 10.0) | ||
| MG_FAR_piglet_addfeedage (%) | 0.270 | |||
| <3 | 0 ( 0.0) | 1 ( 5.0) | ||
| <7 | 14 ( 60.9) | 8 ( 40.0) | ||
| >20 | 1 ( 4.3) | 0 ( 0.0) | ||
| 7-14 | 8 ( 34.8) | 11 ( 55.0) | ||
| MG_ind_feed = 1 (%) | 17 ( 73.9) | 14 ( 70.0) | 1.000 | |
| MG_BR_bedtype_NO (%) | 0.229 | |||
| 0 | 7 ( 30.4) | 11 ( 55.0) | ||
| 1 | 7 ( 30.4) | 4 ( 20.0) | ||
| 2 | 1 ( 4.3) | 4 ( 20.0) | ||
| 5 | 1 ( 4.3) | 0 ( 0.0) | ||
| 12 | 3 ( 13.0) | 1 ( 5.0) | ||
| 14 | 2 ( 8.7) | 0 ( 0.0) | ||
| 25 | 1 ( 4.3) | 0 ( 0.0) | ||
| 125 | 1 ( 4.3) | 0 ( 0.0) | ||
| MG_BR_bedny = 1 (%) | 16 ( 69.6) | 9 ( 45.0) | 0.187 | |
| MG_BR_amount (%) | 0.393 | |||
| 0 | 2 ( 8.7) | 5 ( 25.0) | ||
| 1 | 5 ( 21.7) | 1 ( 5.0) | ||
| 2 | 3 ( 13.0) | 3 ( 15.0) | ||
| 3 | 7 ( 30.4) | 5 ( 25.0) | ||
| 4 | 6 ( 26.1) | 6 ( 30.0) | ||
| MG_BR_rootny = 1 (%) | 17 ( 73.9) | 8 ( 40.0) | 0.053 | |
| MG_BR_toyny (%) | 0.090 | |||
| 0 | 16 ( 69.6) | 8 ( 40.0) | ||
| 1 | 6 ( 26.1) | 10 ( 50.0) | ||
| 4 | 1 ( 4.3) | 0 ( 0.0) | ||
| y | 0 ( 0.0) | 2 ( 10.0) | ||
| MG_sickpen_yn = 1 (%) | 18 ( 78.3) | 15 ( 75.0) | 1.000 | |
| MG_BR_animdirtmed = 2 (%) | 8 ( 36.4) | 9 ( 52.9) | 0.478 | |
| MG_BR_feedtype (%) | 0.145 | |||
| 1 | 1 ( 4.3) | 0 ( 0.0) | ||
| 2 | 0 ( 0.0) | 2 ( 10.0) | ||
| 3 | 3 ( 13.0) | 0 ( 0.0) | ||
| 4 | 18 ( 78.3) | 18 ( 90.0) | ||
| 25 | 1 ( 4.3) | 0 ( 0.0) | ||
| MG_BR_feedclean (%) | 0.246 | |||
| 0 | 20 ( 87.0) | 15 ( 75.0) | ||
| 1 | 2 ( 8.7) | 5 ( 25.0) | ||
| no | 1 ( 4.3) | 0 ( 0.0) | ||
| MG_BR_calm (%) | 0.506 | |||
| 0 | 1 ( 4.3) | 0 ( 0.0) | ||
| 1 | 21 ( 91.3) | 18 ( 90.0) | ||
| 2 | 1 ( 4.3) | 2 ( 10.0) | ||
| MG_BR_dirtanimmed = 2 (%) | 9 ( 42.9) | 10 ( 52.6) | 0.763 | |
| MG_BR_ster = 1 (%) | 4 ( 17.4) | 2 ( 10.0) | 0.798 | |
| MG_PR_type (%) | 0.295 | |||
| 1 | 8 ( 34.8) | 4 ( 20.0) | ||
| 2 | 8 ( 34.8) | 10 ( 50.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.0) | ||
| 12 | 3 ( 13.0) | 0 ( 0.0) | ||
| 13 | 3 ( 13.0) | 2 ( 10.0) | ||
| 14 | 1 ( 4.3) | 0 ( 0.0) | ||
| 23 | 0 ( 0.0) | 1 ( 5.0) | ||
| 123 | 0 ( 0.0) | 1 ( 5.0) | ||
| 124 | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_PR_rootyn = 1 (%) | 20 ( 87.0) | 16 ( 80.0) | 0.840 | |
| MG_PR_toyyn = 1 (%) | 7 ( 30.4) | 9 ( 45.0) | 0.503 | |
| MG_PR_toy (%) | 0.270 | |||
| 0 | 16 ( 69.6) | 11 ( 55.0) | ||
| 1 | 0 ( 0.0) | 1 ( 5.0) | ||
| 2 | 3 ( 13.0) | 0 ( 0.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.0) | ||
| 4 | 4 ( 17.4) | 4 ( 20.0) | ||
| 5 | 0 ( 0.0) | 1 ( 5.0) | ||
| 14 | 0 ( 0.0) | 1 ( 5.0) | ||
| 24 | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_rootamount (%) | 0.626 | |||
| 0 | 3 ( 13.0) | 4 ( 20.0) | ||
| 1 | 10 ( 43.5) | 6 ( 30.0) | ||
| 2 | 10 ( 43.5) | 10 ( 50.0) | ||
| MG_PR_kuivaliete (%) | 0.133 | |||
| 1 | 7 ( 30.4) | 6 ( 30.0) | ||
| 2 | 12 ( 52.2) | 14 ( 70.0) | ||
| 12 | 4 ( 17.4) | 0 ( 0.0) | ||
| MG_PR_ruok_0nonlock_1lock (%) | 0.248 | |||
| 0 | 10 ( 43.5) | 12 ( 60.0) | ||
| 1 | 13 ( 56.5) | 7 ( 35.0) | ||
| 3 | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_PR_feedtype (%) | 0.354 | |||
| 1 | 2 ( 8.7) | 1 ( 5.0) | ||
| 2 | 1 ( 4.3) | 4 ( 20.0) | ||
| 3 | 4 ( 17.4) | 4 ( 20.0) | ||
| 4 | 13 ( 56.5) | 7 ( 35.0) | ||
| 5 | 1 ( 4.3) | 3 ( 15.0) | ||
| 6 | 0 ( 0.0) | 1 ( 5.0) | ||
| 25 | 1 ( 4.3) | 0 ( 0.0) | ||
| 34 | 1 ( 4.3) | 0 ( 0.0) | ||
| MG_PR_calm = 2 (%) | 1 ( 4.3) | 2 ( 10.0) | 0.900 | |
| MG_PR_animdirtmed = 2 (%) | 6 ( 26.1) | 9 ( 47.4) | 0.267 | |
| MG_PR_ster = 1 (%) | 2 ( 8.7) | 2 ( 10.0) | 1.000 | |
| MG_FAR_bed_yn = 1 (%) | 17 ( 73.9) | 16 ( 80.0) | 0.913 | |
| MG_FAR_bed12345_NO (%) | 0.374 | |||
| 0 | 6 ( 26.1) | 4 ( 20.0) | ||
| 1 | 2 ( 8.7) | 1 ( 5.0) | ||
| 2 | 4 ( 17.4) | 7 ( 35.0) | ||
| 4 | 1 ( 4.3) | 0 ( 0.0) | ||
| 5 | 1 ( 4.3) | 0 ( 0.0) | ||
| 12 | 6 ( 26.1) | 4 ( 20.0) | ||
| 14 | 2 ( 8.7) | 0 ( 0.0) | ||
| 15 | 1 ( 4.3) | 0 ( 0.0) | ||
| 24 | 0 ( 0.0) | 1 ( 5.0) | ||
| 25 | 0 ( 0.0) | 2 ( 10.0) | ||
| 245 | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_FAR_bedamount (%) | 0.150 | |||
| 0 | 0 ( 0.0) | 1 ( 5.0) | ||
| 1 | 4 ( 17.4) | 1 ( 5.0) | ||
| 2 | 3 ( 13.0) | 8 ( 40.0) | ||
| 3 | 9 ( 39.1) | 7 ( 35.0) | ||
| 4 | 7 ( 30.4) | 3 ( 15.0) | ||
| MG_FAR_root_yn = 1 (%) | 18 ( 78.3) | 17 ( 85.0) | 0.862 | |
| MG_FAR_toy = 1 (%) | 11 ( 47.8) | 12 ( 60.0) | 0.623 | |
| MG_FAR_rootamount (%) | 0.263 | |||
| 0 | 2 ( 8.7) | 3 ( 15.0) | ||
| 1 | 5 ( 21.7) | 1 ( 5.0) | ||
| 2 | 16 ( 69.6) | 16 ( 80.0) | ||
| MG_FAR_dirtmed = 2 (%) | 7 ( 33.3) | 8 ( 47.1) | 0.598 | |
| MG_FAR_diranimmed = 2 (%) | 9 ( 45.0) | 8 ( 44.4) | 1.000 | |
| MG_FAR_toytoinen_MIKA_NO (%) | 0.525 | |||
| 2 ( 8.7) | 0 ( 0.0) | |||
| 0 | 1 ( 4.3) | 1 ( 5.0) | ||
| 1 | 6 ( 26.1) | 3 ( 15.0) | ||
| 2 | 11 ( 47.8) | 14 ( 70.0) | ||
| 3 | 2 ( 8.7) | 2 ( 10.0) | ||
| noinfo | 1 ( 4.3) | 0 ( 0.0) | ||
| MG_SOWSperworkeredit_NUM57_113_147_NO (%) | 0.455 | |||
| 11 | 1 ( 4.3) | 0 ( 0.0) | ||
| 20 | 1 ( 4.3) | 0 ( 0.0) | ||
| 23 | 1 ( 4.3) | 0 ( 0.0) | ||
| 25 | 0 ( 0.0) | 1 ( 5.0) | ||
| 26 | 1 ( 4.3) | 0 ( 0.0) | ||
| 29 | 1 ( 4.3) | 0 ( 0.0) | ||
| 38 | 0 ( 0.0) | 1 ( 5.0) | ||
| 46 | 1 ( 4.3) | 0 ( 0.0) | ||
| 49 | 0 ( 0.0) | 1 ( 5.0) | ||
| 56 | 1 ( 4.3) | 0 ( 0.0) | ||
| 57 | 1 ( 4.3) | 1 ( 5.0) | ||
| 62 | 0 ( 0.0) | 1 ( 5.0) | ||
| 85 | 0 ( 0.0) | 1 ( 5.0) | ||
| 86 | 1 ( 4.3) | 0 ( 0.0) | ||
| 88 | 2 ( 8.7) | 1 ( 5.0) | ||
| 101 | 1 ( 4.3) | 0 ( 0.0) | ||
| 106 | 1 ( 4.3) | 0 ( 0.0) | ||
| 113 | 2 ( 8.7) | 0 ( 0.0) | ||
| 116 | 0 ( 0.0) | 1 ( 5.0) | ||
| 117 | 1 ( 4.3) | 0 ( 0.0) | ||
| 126 | 0 ( 0.0) | 1 ( 5.0) | ||
| 129 | 1 ( 4.3) | 0 ( 0.0) | ||
| 131 | 0 ( 0.0) | 1 ( 5.0) | ||
| 132 | 1 ( 4.3) | 0 ( 0.0) | ||
| 133 | 0 ( 0.0) | 1 ( 5.0) | ||
| 134 | 1 ( 4.3) | 0 ( 0.0) | ||
| 135 | 0 ( 0.0) | 1 ( 5.0) | ||
| 137 | 0 ( 0.0) | 1 ( 5.0) | ||
| 158 | 1 ( 4.3) | 0 ( 0.0) | ||
| 163 | 0 ( 0.0) | 1 ( 5.0) | ||
| 165 | 0 ( 0.0) | 1 ( 5.0) | ||
| 167 | 1 ( 4.3) | 0 ( 0.0) | ||
| 181 | 1 ( 4.3) | 0 ( 0.0) | ||
| 185 | 0 ( 0.0) | 1 ( 5.0) | ||
| 192 | 0 ( 0.0) | 1 ( 5.0) | ||
| 196 | 0 ( 0.0) | 1 ( 5.0) | ||
| 340 | 0 ( 0.0) | 1 ( 5.0) | ||
| 342 | 1 ( 4.3) | 0 ( 0.0) | ||
| 395 | 0 ( 0.0) | 1 ( 5.0) | ||
| MG_SOWSperworkeredit_57_113_147_ (%) | 0.253 | |||
| 1 | 8 ( 34.8) | 4 ( 20.0) | ||
| 2 | 7 ( 30.4) | 3 ( 15.0) | ||
| 3 | 5 ( 21.7) | 7 ( 35.0) | ||
| 4 | 3 ( 13.0) | 6 ( 30.0) | ||
| OUT_SOW_mort_dic = 1 (%) | 0 ( 0.0) | 20 (100.0) | <0.001 | |
| OUT_SOW_totrem_dic = 1 (%) | 7 ( 30.4) | 15 ( 75.0) | 0.009 | |
| OUT_SOW_cull_dic = 1 (%) | 11 ( 47.8) | 10 ( 50.0) | 1.000 |
Yhteenveto poistojen mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')table2%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 22 | 21 | ||
| MG_BR_giltpurchage_NUM_NO (mean (sd)) | 3.45 (2.74) | 1.95 (1.88) | 0.043 | |
| MG_BR_heatdetec_startNUM_NO (mean (sd)) | 2.91 (2.20) | 4.00 (2.49) | 0.135 | |
| MG_BR_farmsemenNUM_NO (mean (sd)) | 1.36 (1.05) | 2.62 (2.52) | 0.037 | |
| MG_BR_insonceNUM_NO (mean (sd)) | 5.86 (4.16) | 4.14 (3.64) | 0.157 | |
| MG_FAR_ToFarunitNUM_NO (mean (sd)) | 2.77 (1.38) | 3.38 (1.50) | 0.173 | |
| MG_FAR_NestmatdaysNUM_NO (mean (sd)) | 4.55 (2.09) | 4.24 (2.41) | 0.656 | |
| MG_FAR_piglet_rem_ageNUM_NO (mean (sd)) | 3.64 (1.62) | 3.24 (2.10) | 0.488 | |
| MG_FAR_pigletremaount_NUM_NO (mean (sd)) | 11.64 (6.24) | 12.57 (7.40) | 0.656 | |
| MG_BR_dirt_NUM_NO (mean (sd)) | 2.26 (1.19) | 2.55 (1.79) | 0.562 | |
| MG_BR_dirtanim_NUM_NO (mean (sd)) | 2.80 (1.28) | 2.50 (1.50) | 0.501 | |
| MG_PR_dirt_NUM_NO (mean (sd)) | 3.33 (1.93) | 3.81 (2.50) | 0.494 | |
| MG_PR_late_HAR_kaNUM_NO (mean (sd)) | 6.41 (4.96) | 4.29 (5.11) | 0.174 | |
| MG_FAR_toynum_NO (mean (sd)) | 3.85 (1.95) | 4.27 (1.67) | 0.544 | |
| MG_FAR_dirt_NUM_NO (mean (sd)) | 2.16 (1.26) | 2.58 (1.61) | 0.375 | |
| MG_FAR_diranim_NUM_NO (mean (sd)) | 2.63 (1.54) | 3.21 (2.15) | 0.346 | |
| MG_BR_sowsperboar_NUM_NO (mean (sd)) | 18.41 (10.39) | 18.95 (10.89) | 0.868 | |
| MG_SOWSperworker_NUM_NO (mean (sd)) | 16.71 (10.34) | 20.40 (9.64) | 0.245 | |
| MG_PR_earlyHAR_kaNUM (mean (sd)) | 8.59 (4.56) | 9.33 (5.74) | 0.640 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 8.73 (5.55) | 8.76 (4.73) | 0.983 | |
| OUT_SOW_totremproNUM (mean (sd)) | 7.77 (5.46) | 18.62 (6.82) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 7.18 (3.57) | 20.00 (4.27) | <0.001 | |
| MG_feedtimes_preg (%) | 0.277 | |||
| 1 | 0 ( 0.0) | 2 ( 10.5) | ||
| 2 | 20 (90.9) | 14 ( 73.7) | ||
| 3 | 2 ( 9.1) | 2 ( 10.5) | ||
| 4 | 0 ( 0.0) | 1 ( 5.3) | ||
| MG_feedtimes_far (%) | 0.035 | |||
| 2 | 4 (18.2) | 0 ( 0.0) | ||
| 3 | 12 (54.5) | 17 ( 89.5) | ||
| 4 | 6 (27.3) | 2 ( 10.5) | ||
| MG_feed_liq_solid (%) | 0.531 | |||
| 1 ( 4.5) | 2 ( 9.5) | |||
| liq | 12 (54.5) | 12 ( 57.1) | ||
| liqsol | 0 ( 0.0) | 2 ( 9.5) | ||
| sol | 7 (31.8) | 4 ( 19.0) | ||
| solid | 1 ( 4.5) | 1 ( 4.8) | ||
| solliq | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_owngilts (%) | 0.275 | |||
| 0 | 8 (36.4) | 7 ( 33.3) | ||
| 50 | 1 ( 4.5) | 0 ( 0.0) | ||
| 70 | 1 ( 4.5) | 0 ( 0.0) | ||
| 80 | 1 ( 4.5) | 1 ( 4.8) | ||
| 90 | 3 (13.6) | 0 ( 0.0) | ||
| 95 | 0 ( 0.0) | 2 ( 9.5) | ||
| 100 | 8 (36.4) | 11 ( 52.4) | ||
| MG_owngilts.1 = 1 (%) | 14 (63.6) | 14 ( 66.7) | 1.000 | |
| MG_BR_giltchangebeforeins_NO = 1 (%) | 8 (36.4) | 11 ( 52.4) | 0.453 | |
| MG_BR_giltflush_NO (%) | 0.128 | |||
| 0 | 15 (68.2) | 8 ( 38.1) | ||
| 1 | 6 (27.3) | 12 ( 57.1) | ||
| noneed | 1 ( 4.5) | 1 ( 4.8) | ||
| MG_BR_giltboarstart_NO (%) | 0.530 | |||
| 0 | 2 ( 9.1) | 2 ( 9.5) | ||
| 3 | 1 ( 4.5) | 0 ( 0.0) | ||
| 4 | 1 ( 4.5) | 0 ( 0.0) | ||
| 6 | 5 (22.7) | 3 ( 14.3) | ||
| 6,5 | 1 ( 4.5) | 2 ( 9.5) | ||
| 7 | 9 (40.9) | 7 ( 33.3) | ||
| 7,5 | 2 ( 9.1) | 6 ( 28.6) | ||
| 8 | 1 ( 4.5) | 0 ( 0.0) | ||
| noinfo | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_BR_giltinsage_NO (%) | 0.771 | |||
| 0 | 2 ( 9.1) | 1 ( 4.8) | ||
| 2ndheat | 1 ( 4.5) | 0 ( 0.0) | ||
| 6 | 1 ( 4.5) | 0 ( 0.0) | ||
| 7 | 1 ( 4.5) | 1 ( 4.8) | ||
| 7,5 | 1 ( 4.5) | 1 ( 4.8) | ||
| 8 | 12 (54.5) | 13 ( 61.9) | ||
| 8,5 | 1 ( 4.5) | 3 ( 14.3) | ||
| 9 | 1 ( 4.5) | 0 ( 0.0) | ||
| 9,5 | 2 ( 9.1) | 1 ( 4.8) | ||
| noinfo | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_BR_heatgroup_NO (%) | 0.426 | |||
| 0 | 3 (13.6) | 6 ( 28.6) | ||
| 1 | 18 (81.8) | 13 ( 61.9) | ||
| no | 0 ( 0.0) | 1 ( 4.8) | ||
| noinfo | 1 ( 4.5) | 1 ( 4.8) | ||
| MG_BR_heatmarkback_NO (%) | 0.044 | |||
| 0 | 9 (40.9) | 2 ( 9.5) | ||
| 1 | 13 (59.1) | 18 ( 85.7) | ||
| noinfo | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_BR_artinspro_050_5099_100 (%) | 0.666 | |||
| 0 | 1 ( 4.5) | 1 ( 4.8) | ||
| 1 | 8 (36.4) | 5 ( 23.8) | ||
| 2 | 13 (59.1) | 15 ( 71.4) | ||
| MG_BR_once_012 (%) | 0.894 | |||
| 0 | 7 (33.3) | 8 ( 40.0) | ||
| 1 | 12 (57.1) | 10 ( 50.0) | ||
| 2 | 2 ( 9.5) | 2 ( 10.0) | ||
| MG_BR_instriple_NO (%) | 0.432 | |||
| 0 | 7 (31.8) | 4 ( 19.0) | ||
| 1 | 2 ( 9.1) | 1 ( 4.8) | ||
| 10 | 4 (18.2) | 7 ( 33.3) | ||
| 15 | 0 ( 0.0) | 2 ( 9.5) | ||
| 2 | 1 ( 4.5) | 0 ( 0.0) | ||
| 3 | 2 ( 9.1) | 0 ( 0.0) | ||
| 30 | 0 ( 0.0) | 1 ( 4.8) | ||
| 33 | 0 ( 0.0) | 1 ( 4.8) | ||
| 5 | 5 (22.7) | 4 ( 19.0) | ||
| noinfo | 1 ( 4.5) | 1 ( 4.8) | ||
| MG_BR_triple_012 (%) | 0.175 | |||
| 0 | 7 (31.8) | 4 ( 19.0) | ||
| 1 | 14 (63.6) | 12 ( 57.1) | ||
| 2 | 0 ( 0.0) | 4 ( 19.0) | ||
| noinfo | 1 ( 4.5) | 1 ( 4.8) | ||
| MG_aveins (%) | 0.367 | |||
| 1 | 1 ( 4.5) | 0 ( 0.0) | ||
| 2 | 21 (95.5) | 20 ( 95.2) | ||
| 2,1 | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_BR_nopregus (%) | 0.099 | |||
| 0 | 7 (31.8) | 6 ( 28.6) | ||
| 1 | 14 (63.6) | 8 ( 38.1) | ||
| 2 | 1 ( 4.5) | 6 ( 28.6) | ||
| noinfo | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_FAR_ind_0no_1rout_2sometimes (%) | 0.164 | |||
| 0 | 11 (50.0) | 6 ( 28.6) | ||
| 1 | 0 ( 0.0) | 2 ( 9.5) | ||
| 2 | 11 (50.0) | 13 ( 61.9) | ||
| MG_FAR_nestmatamount (%) | 0.038 | |||
| 0 | 2 ( 9.1) | 3 ( 14.3) | ||
| 1 | 4 (18.2) | 0 ( 0.0) | ||
| 2 | 14 (63.6) | 10 ( 47.6) | ||
| 3 | 2 ( 9.1) | 8 ( 38.1) | ||
| MG_FAR_nestmat_NO (%) | 0.210 | |||
| _CUT | 2 ( 9.1) | 0 ( 0.0) | ||
| _NWS | 2 ( 9.1) | 3 ( 14.3) | ||
| 0 | 2 ( 9.1) | 3 ( 14.3) | ||
| heiina | 1 ( 4.5) | 0 ( 0.0) | ||
| heina | 0 ( 0.0) | 1 ( 4.8) | ||
| heina puruturve | 0 ( 0.0) | 1 ( 4.8) | ||
| heina turve | 1 ( 4.5) | 0 ( 0.0) | ||
| no | 0 ( 0.0) | 1 ( 4.8) | ||
| STR | 11 (50.0) | 6 ( 28.6) | ||
| STR_CUT | 1 ( 4.5) | 6 ( 28.6) | ||
| STR_CUT_NWS | 1 ( 4.5) | 0 ( 0.0) | ||
| STR_NWS | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_FAR_ox_0_13_46_7 (%) | 0.652 | |||
| 0 | 1 ( 4.5) | 0 ( 0.0) | ||
| 1 | 9 (40.9) | 9 ( 45.0) | ||
| 2 | 4 (18.2) | 2 ( 10.0) | ||
| 3 | 8 (36.4) | 9 ( 45.0) | ||
| MG_FAR_obstex_preox (%) | 0.541 | |||
| 0 | 13 (59.1) | 13 ( 61.9) | ||
| 1 | 9 (40.9) | 7 ( 33.3) | ||
| noinfo | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_FAR_far_assist_CAT (%) | 0.289 | |||
| <6 | 6 (27.3) | 5 ( 23.8) | ||
| 10 | 0 ( 0.0) | 1 ( 4.8) | ||
| 15 | 1 ( 4.5) | 0 ( 0.0) | ||
| 20-50 | 4 (18.2) | 4 ( 19.0) | ||
| 50 | 4 (18.2) | 0 ( 0.0) | ||
| 6-20 | 6 (27.3) | 8 ( 38.1) | ||
| noinfo | 1 ( 4.5) | 3 ( 14.3) | ||
| MG_FAR_farassist_MAY_NO (%) | 0.463 | |||
| 0 ( 0.0) | 2 ( 9.5) | |||
| _GLO_LUBR | 9 (40.9) | 6 ( 28.6) | ||
| _HANDWASH_GLO_LUBR | 0 ( 0.0) | 1 ( 4.8) | ||
| GLO_LUBR | 0 ( 0.0) | 1 ( 4.8) | ||
| WASH_GLO_LUBR | 6 (27.3) | 7 ( 33.3) | ||
| WASH_HANDWASH_GLO_LUBR | 6 (27.3) | 3 ( 14.3) | ||
| WASH_HANDWASH_LUBR | 1 ( 4.5) | 1 ( 4.8) | ||
| MG_FAR_piglet_rem_amountCAT (%) | 0.088 | |||
| 0 | 0 ( 0.0) | 1 ( 4.8) | ||
| 1 | 10 (45.5) | 5 ( 23.8) | ||
| 2 | 6 (27.3) | 5 ( 23.8) | ||
| 3 | 5 (22.7) | 2 ( 9.5) | ||
| 4 | 1 ( 4.5) | 6 ( 28.6) | ||
| noinfo | 0 ( 0.0) | 2 ( 9.5) | ||
| MG_FAR_piglet_addfeedage (%) | 0.530 | |||
| <3 | 0 ( 0.0) | 1 ( 4.8) | ||
| <7 | 12 (54.5) | 10 ( 47.6) | ||
| >20 | 1 ( 4.5) | 0 ( 0.0) | ||
| 7-14 | 9 (40.9) | 10 ( 47.6) | ||
| MG_ind_feed = 1 (%) | 16 (72.7) | 15 ( 71.4) | 1.000 | |
| MG_BR_bedtype_NO (%) | 0.529 | |||
| 0 | 8 (36.4) | 10 ( 47.6) | ||
| 1 | 6 (27.3) | 5 ( 23.8) | ||
| 2 | 4 (18.2) | 1 ( 4.8) | ||
| 5 | 0 ( 0.0) | 1 ( 4.8) | ||
| 12 | 1 ( 4.5) | 3 ( 14.3) | ||
| 14 | 1 ( 4.5) | 1 ( 4.8) | ||
| 25 | 1 ( 4.5) | 0 ( 0.0) | ||
| 125 | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_BR_bedny = 1 (%) | 14 (63.6) | 11 ( 52.4) | 0.661 | |
| MG_BR_amount (%) | 0.294 | |||
| 0 | 2 ( 9.1) | 5 ( 23.8) | ||
| 1 | 2 ( 9.1) | 4 ( 19.0) | ||
| 2 | 5 (22.7) | 1 ( 4.8) | ||
| 3 | 7 (31.8) | 5 ( 23.8) | ||
| 4 | 6 (27.3) | 6 ( 28.6) | ||
| MG_BR_rootny = 1 (%) | 14 (63.6) | 11 ( 52.4) | 0.661 | |
| MG_BR_toyny (%) | 0.450 | |||
| 0 | 14 (63.6) | 10 ( 47.6) | ||
| 1 | 6 (27.3) | 10 ( 47.6) | ||
| 4 | 1 ( 4.5) | 0 ( 0.0) | ||
| y | 1 ( 4.5) | 1 ( 4.8) | ||
| MG_sickpen_yn = 1 (%) | 18 (81.8) | 15 ( 71.4) | 0.656 | |
| MG_BR_animdirtmed = 2 (%) | 8 (42.1) | 9 ( 45.0) | 1.000 | |
| MG_BR_feedtype (%) | 0.115 | |||
| 1 | 1 ( 4.5) | 0 ( 0.0) | ||
| 2 | 2 ( 9.1) | 0 ( 0.0) | ||
| 3 | 3 (13.6) | 0 ( 0.0) | ||
| 4 | 16 (72.7) | 20 ( 95.2) | ||
| 25 | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_BR_feedclean (%) | 0.563 | |||
| 0 | 18 (81.8) | 17 ( 81.0) | ||
| 1 | 3 (13.6) | 4 ( 19.0) | ||
| no | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_BR_calm (%) | 0.513 | |||
| 0 | 0 ( 0.0) | 1 ( 4.8) | ||
| 1 | 20 (90.9) | 19 ( 90.5) | ||
| 2 | 2 ( 9.1) | 1 ( 4.8) | ||
| MG_BR_dirtanimmed = 2 (%) | 11 (55.0) | 8 ( 40.0) | 0.527 | |
| MG_BR_ster = 1 (%) | 2 ( 9.1) | 4 ( 19.0) | 0.616 | |
| MG_PR_type (%) | 0.640 | |||
| 1 | 5 (22.7) | 7 ( 33.3) | ||
| 2 | 10 (45.5) | 8 ( 38.1) | ||
| 3 | 0 ( 0.0) | 1 ( 4.8) | ||
| 12 | 1 ( 4.5) | 2 ( 9.5) | ||
| 13 | 3 (13.6) | 2 ( 9.5) | ||
| 14 | 1 ( 4.5) | 0 ( 0.0) | ||
| 23 | 1 ( 4.5) | 0 ( 0.0) | ||
| 123 | 0 ( 0.0) | 1 ( 4.8) | ||
| 124 | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_PR_rootyn = 1 (%) | 20 (90.9) | 16 ( 76.2) | 0.372 | |
| MG_PR_toyyn = 1 (%) | 6 (27.3) | 10 ( 47.6) | 0.287 | |
| MG_PR_toy (%) | 0.456 | |||
| 0 | 16 (72.7) | 11 ( 52.4) | ||
| 1 | 0 ( 0.0) | 1 ( 4.8) | ||
| 2 | 1 ( 4.5) | 2 ( 9.5) | ||
| 3 | 0 ( 0.0) | 1 ( 4.8) | ||
| 4 | 3 (13.6) | 5 ( 23.8) | ||
| 5 | 1 ( 4.5) | 0 ( 0.0) | ||
| 14 | 0 ( 0.0) | 1 ( 4.8) | ||
| 24 | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_rootamount (%) | 0.356 | |||
| 0 | 2 ( 9.1) | 5 ( 23.8) | ||
| 1 | 8 (36.4) | 8 ( 38.1) | ||
| 2 | 12 (54.5) | 8 ( 38.1) | ||
| MG_PR_kuivaliete (%) | 0.663 | |||
| 1 | 8 (36.4) | 5 ( 23.8) | ||
| 2 | 12 (54.5) | 14 ( 66.7) | ||
| 12 | 2 ( 9.1) | 2 ( 9.5) | ||
| MG_PR_ruok_0nonlock_1lock (%) | 0.560 | |||
| 0 | 12 (54.5) | 10 ( 47.6) | ||
| 1 | 10 (45.5) | 10 ( 47.6) | ||
| 3 | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_PR_feedtype (%) | 0.646 | |||
| 1 | 1 ( 4.5) | 2 ( 9.5) | ||
| 2 | 4 (18.2) | 1 ( 4.8) | ||
| 3 | 4 (18.2) | 4 ( 19.0) | ||
| 4 | 10 (45.5) | 10 ( 47.6) | ||
| 5 | 2 ( 9.1) | 2 ( 9.5) | ||
| 6 | 0 ( 0.0) | 1 ( 4.8) | ||
| 25 | 0 ( 0.0) | 1 ( 4.8) | ||
| 34 | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_PR_calm = 2 (%) | 0 ( 0.0) | 3 ( 14.3) | 0.215 | |
| MG_PR_animdirtmed = 2 (%) | 6 (28.6) | 9 ( 42.9) | 0.520 | |
| MG_PR_ster = 1 (%) | 2 ( 9.1) | 2 ( 9.5) | 1.000 | |
| MG_FAR_bed_yn = 1 (%) | 18 (81.8) | 15 ( 71.4) | 0.656 | |
| MG_FAR_bed12345_NO (%) | 0.798 | |||
| 0 | 4 (18.2) | 6 ( 28.6) | ||
| 1 | 2 ( 9.1) | 1 ( 4.8) | ||
| 2 | 5 (22.7) | 6 ( 28.6) | ||
| 4 | 0 ( 0.0) | 1 ( 4.8) | ||
| 5 | 1 ( 4.5) | 0 ( 0.0) | ||
| 12 | 6 (27.3) | 4 ( 19.0) | ||
| 14 | 1 ( 4.5) | 1 ( 4.8) | ||
| 15 | 1 ( 4.5) | 0 ( 0.0) | ||
| 24 | 0 ( 0.0) | 1 ( 4.8) | ||
| 25 | 1 ( 4.5) | 1 ( 4.8) | ||
| 245 | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_FAR_bedamount (%) | 0.538 | |||
| 0 | 0 ( 0.0) | 1 ( 4.8) | ||
| 1 | 4 (18.2) | 1 ( 4.8) | ||
| 2 | 6 (27.3) | 5 ( 23.8) | ||
| 3 | 7 (31.8) | 9 ( 42.9) | ||
| 4 | 5 (22.7) | 5 ( 23.8) | ||
| MG_FAR_root_yn = 1 (%) | 19 (86.4) | 16 ( 76.2) | 0.642 | |
| MG_FAR_toy = 1 (%) | 10 (45.5) | 13 ( 61.9) | 0.438 | |
| MG_FAR_rootamount (%) | 0.277 | |||
| 0 | 1 ( 4.5) | 4 ( 19.0) | ||
| 1 | 4 (18.2) | 2 ( 9.5) | ||
| 2 | 17 (77.3) | 15 ( 71.4) | ||
| MG_FAR_dirtmed = 2 (%) | 7 (36.8) | 8 ( 42.1) | 1.000 | |
| MG_FAR_diranimmed = 2 (%) | 7 (36.8) | 10 ( 52.6) | 0.514 | |
| MG_FAR_toytoinen_MIKA_NO (%) | 0.801 | |||
| 1 ( 4.5) | 1 ( 4.8) | |||
| 0 | 1 ( 4.5) | 1 ( 4.8) | ||
| 1 | 3 (13.6) | 6 ( 28.6) | ||
| 2 | 14 (63.6) | 11 ( 52.4) | ||
| 3 | 2 ( 9.1) | 2 ( 9.5) | ||
| noinfo | 1 ( 4.5) | 0 ( 0.0) | ||
| MG_SOWSperworkeredit_NUM57_113_147_NO (%) | 0.547 | |||
| 11 | 1 ( 4.5) | 0 ( 0.0) | ||
| 20 | 1 ( 4.5) | 0 ( 0.0) | ||
| 23 | 0 ( 0.0) | 1 ( 4.8) | ||
| 25 | 0 ( 0.0) | 1 ( 4.8) | ||
| 26 | 1 ( 4.5) | 0 ( 0.0) | ||
| 29 | 1 ( 4.5) | 0 ( 0.0) | ||
| 38 | 1 ( 4.5) | 0 ( 0.0) | ||
| 46 | 1 ( 4.5) | 0 ( 0.0) | ||
| 49 | 1 ( 4.5) | 0 ( 0.0) | ||
| 56 | 1 ( 4.5) | 0 ( 0.0) | ||
| 57 | 1 ( 4.5) | 1 ( 4.8) | ||
| 62 | 1 ( 4.5) | 0 ( 0.0) | ||
| 85 | 1 ( 4.5) | 0 ( 0.0) | ||
| 86 | 0 ( 0.0) | 1 ( 4.8) | ||
| 88 | 1 ( 4.5) | 2 ( 9.5) | ||
| 101 | 0 ( 0.0) | 1 ( 4.8) | ||
| 106 | 0 ( 0.0) | 1 ( 4.8) | ||
| 113 | 1 ( 4.5) | 1 ( 4.8) | ||
| 116 | 1 ( 4.5) | 0 ( 0.0) | ||
| 117 | 0 ( 0.0) | 1 ( 4.8) | ||
| 126 | 1 ( 4.5) | 0 ( 0.0) | ||
| 129 | 1 ( 4.5) | 0 ( 0.0) | ||
| 131 | 0 ( 0.0) | 1 ( 4.8) | ||
| 132 | 0 ( 0.0) | 1 ( 4.8) | ||
| 133 | 1 ( 4.5) | 0 ( 0.0) | ||
| 134 | 1 ( 4.5) | 0 ( 0.0) | ||
| 135 | 1 ( 4.5) | 0 ( 0.0) | ||
| 137 | 1 ( 4.5) | 0 ( 0.0) | ||
| 158 | 1 ( 4.5) | 0 ( 0.0) | ||
| 163 | 0 ( 0.0) | 1 ( 4.8) | ||
| 165 | 0 ( 0.0) | 1 ( 4.8) | ||
| 167 | 0 ( 0.0) | 1 ( 4.8) | ||
| 181 | 1 ( 4.5) | 0 ( 0.0) | ||
| 185 | 0 ( 0.0) | 1 ( 4.8) | ||
| 192 | 0 ( 0.0) | 1 ( 4.8) | ||
| 196 | 0 ( 0.0) | 1 ( 4.8) | ||
| 340 | 0 ( 0.0) | 1 ( 4.8) | ||
| 342 | 0 ( 0.0) | 1 ( 4.8) | ||
| 395 | 0 ( 0.0) | 1 ( 4.8) | ||
| MG_SOWSperworkeredit_57_113_147_ (%) | 0.017 | |||
| 1 | 9 (40.9) | 3 ( 14.3) | ||
| 2 | 4 (18.2) | 6 ( 28.6) | ||
| 3 | 8 (36.4) | 4 ( 19.0) | ||
| 4 | 1 ( 4.5) | 8 ( 38.1) | ||
| OUT_SOW_mort_dic = 1 (%) | 10 (45.5) | 10 ( 47.6) | 1.000 | |
| OUT_SOW_totrem_dic = 1 (%) | 5 (22.7) | 17 ( 81.0) | <0.001 | |
| OUT_SOW_cull_dic = 1 (%) | 0 ( 0.0) | 21 (100.0) | <0.001 |
Yhteenveto joku hylkays mukaan
res_mca = MCA(medmca, quanti.sup = c(52,53),quali.sup=c(50:51), graph = FALSE)summary(res_mca)##
## Call:
## MCA(X = medmca, quanti.sup = c(52, 53), quali.sup = c(50:51),
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6
## Variance 0.225 0.155 0.143 0.137 0.128 0.115
## % of var. 8.364 5.749 5.308 5.097 4.748 4.278
## Cumulative % of var. 8.364 14.112 19.420 24.517 29.265 33.543
## Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Dim.12
## Variance 0.113 0.102 0.098 0.089 0.086 0.086
## % of var. 4.179 3.782 3.620 3.300 3.205 3.177
## Cumulative % of var. 37.722 41.505 45.124 48.425 51.629 54.806
## Dim.13 Dim.14 Dim.15 Dim.16 Dim.17 Dim.18
## Variance 0.081 0.077 0.072 0.070 0.067 0.064
## % of var. 3.015 2.856 2.683 2.591 2.474 2.388
## Cumulative % of var. 57.821 60.677 63.360 65.950 68.425 70.813
## Dim.19 Dim.20 Dim.21 Dim.22 Dim.23 Dim.24
## Variance 0.062 0.055 0.054 0.052 0.050 0.047
## % of var. 2.313 2.060 2.012 1.931 1.839 1.754
## Cumulative % of var. 73.126 75.186 77.198 79.129 80.969 82.723
## Dim.25 Dim.26 Dim.27 Dim.28 Dim.29 Dim.30
## Variance 0.046 0.044 0.038 0.038 0.035 0.032
## % of var. 1.692 1.619 1.423 1.412 1.316 1.178
## Cumulative % of var. 84.415 86.034 87.457 88.869 90.184 91.363
## Dim.31 Dim.32 Dim.33 Dim.34 Dim.35 Dim.36
## Variance 0.029 0.029 0.027 0.024 0.021 0.020
## % of var. 1.069 1.059 1.003 0.897 0.793 0.749
## Cumulative % of var. 92.432 93.491 94.494 95.390 96.183 96.932
## Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 0.019 0.018 0.014 0.013 0.012 0.008
## % of var. 0.689 0.663 0.530 0.468 0.431 0.286
## Cumulative % of var. 97.622 98.285 98.815 99.283 99.714 100.000
##
## Individuals (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr
## 1 | 0.317 1.037 0.044 | 0.002 0.000
## 2 | -0.440 1.999 0.089 | -0.074 0.083
## 3 | -0.508 2.667 0.068 | -0.237 0.847
## 4 | 0.215 0.478 0.031 | -0.258 1.001
## 5 | 0.690 4.913 0.145 | -0.085 0.107
## 6 | -0.465 2.234 0.077 | -0.233 0.814
## 7 | -0.171 0.301 0.012 | 0.077 0.088
## 8 | -0.037 0.014 0.001 | -0.218 0.714
## 9 | -0.210 0.455 0.016 | 0.019 0.005
## 10 | 0.142 0.207 0.010 | -0.466 3.257
## cos2 Dim.3 ctr cos2
## 1 0.000 | -0.213 0.741 0.020 |
## 2 0.003 | -0.100 0.163 0.005 |
## 3 0.015 | -0.615 6.152 0.100 |
## 4 0.045 | -0.226 0.827 0.034 |
## 5 0.002 | 0.140 0.320 0.006 |
## 6 0.019 | -0.304 1.499 0.033 |
## 7 0.002 | -0.156 0.394 0.010 |
## 8 0.024 | 0.016 0.004 0.000 |
## 9 0.000 | -0.059 0.057 0.001 |
## 10 0.104 | -0.290 1.372 0.040 |
##
## Categories (the 10 first)
## Dim.1 ctr cos2 v.test Dim.2
## MG_feedtimes_preg_1 | -0.345 0.050 0.006 -0.493 | 0.169
## MG_feedtimes_preg_2 | 0.028 0.006 0.003 0.358 | -0.120
## MG_feedtimes_preg_3 | -0.275 0.064 0.008 -0.570 | 0.663
## MG_feedtimes_preg_4 | -0.737 0.114 0.013 -0.737 | 1.163
## MG_feedtimes_preg_Not Assigned | 0.779 0.255 0.030 1.115 | -0.034
## MG_feedtimes_far_2 | -0.706 0.421 0.051 -1.466 | -0.921
## MG_feedtimes_far_3 | 0.012 0.001 0.000 0.115 | 0.100
## MG_feedtimes_far_4 | 0.114 0.022 0.003 0.352 | 0.106
## MG_feedtimes_far_Not Assigned | 0.779 0.255 0.030 1.115 | -0.034
## V10 | 0.619 0.242 0.029 1.098 | -0.417
## ctr cos2 v.test Dim.3 ctr cos2
## MG_feedtimes_preg_1 0.018 0.001 0.242 | -1.191 0.942 0.069
## MG_feedtimes_preg_2 0.150 0.055 -1.514 | 0.014 0.002 0.001
## MG_feedtimes_preg_3 0.539 0.045 1.376 | -0.151 0.030 0.002
## MG_feedtimes_preg_4 0.415 0.032 1.163 | 0.585 0.114 0.008
## MG_feedtimes_preg_Not Assigned 0.001 0.000 -0.048 | 0.960 0.612 0.045
## MG_feedtimes_far_2 1.040 0.087 -1.912 | 1.090 1.576 0.122
## MG_feedtimes_far_3 0.089 0.021 0.935 | -0.144 0.198 0.043
## MG_feedtimes_far_4 0.027 0.003 0.328 | -0.264 0.186 0.016
## MG_feedtimes_far_Not Assigned 0.001 0.000 -0.048 | 0.960 0.612 0.045
## V10 0.160 0.013 -0.740 | 0.384 0.147 0.011
## v.test
## MG_feedtimes_preg_1 -1.705 |
## MG_feedtimes_preg_2 0.179 |
## MG_feedtimes_preg_3 -0.314 |
## MG_feedtimes_preg_4 0.585 |
## MG_feedtimes_preg_Not Assigned 1.374 |
## MG_feedtimes_far_2 2.261 |
## MG_feedtimes_far_3 -1.339 |
## MG_feedtimes_far_4 -0.819 |
## MG_feedtimes_far_Not Assigned 1.374 |
## V10 0.681 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## MG_feedtimes_preg | 0.054 0.085 0.119 |
## MG_feedtimes_far | 0.077 0.088 0.180 |
## MG_feed_liq_solid | 0.180 0.125 0.241 |
## MG_owngilts | 0.227 0.356 0.168 |
## MG_owngilts.1 | 0.155 0.239 0.018 |
## MG_BR_artinspro_050_5099_100 | 0.274 0.339 0.050 |
## MG_BR_once_012 | 0.336 0.183 0.168 |
## MG_BR_triple_012 | 0.412 0.095 0.042 |
## MG_aveins | 0.067 0.411 0.019 |
## MG_BR_nopregus | 0.375 0.024 0.147 |
##
## Supplementary categories
## Dim.1 cos2 v.test Dim.2 cos2
## OUT_SOW_totrem_dic_0 | -0.330 0.104 -2.091 | -0.330 0.104
## OUT_SOW_totrem_dic_1 | 0.315 0.104 2.091 | 0.315 0.104
## OUT_SOW_cull_dic_0 | -0.292 0.089 -1.937 | -0.048 0.002
## OUT_SOW_cull_dic_1 | 0.306 0.089 1.937 | 0.051 0.002
## v.test Dim.3 cos2 v.test
## OUT_SOW_totrem_dic_0 -2.091 | 0.100 0.009 0.631 |
## OUT_SOW_totrem_dic_1 2.091 | -0.095 0.009 -0.631 |
## OUT_SOW_cull_dic_0 -0.320 | 0.119 0.015 0.790 |
## OUT_SOW_cull_dic_1 0.320 | -0.125 0.015 -0.790 |
##
## Supplementary categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## OUT_SOW_totrem_dic | 0.104 0.104 0.009 |
## OUT_SOW_cull_dic | 0.089 0.002 0.015 |
##
## Supplementary continuous variables
## Dim.1 Dim.2 Dim.3
## OUT_SOW_mort_proNUM | 0.304 | 0.285 | -0.090 |
## OUT_SOW_cullproNUM | 0.301 | 0.188 | -0.154 |To visualize the percentage of inertia explained by each MCA dimension:
eig.val <- res_mca$eig
barplot(eig.val[, 2],
names.arg = 1:nrow(eig.val),
main = "Variances Explained by Dimensions (%)",
xlab = "Principal Dimensions",
ylab = "Percentage of variances",
col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2],
type = "b", pch = 19, col = "red")
res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
var_sup = TRUE, var_lab_min_contrib = 0,
col_var = "Variable", symbol_var = "Type",
size_var = NULL, size_range = c(10, 300),
labels_size = 10, point_size = 56,
transitions = TRUE, labels_positions = NULL)res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
lab_var = NULL, , ind_lab_min_contrib = 0,
col_var = NULL, labels_size = 9,
point_opacity = 0.5, opacity_var = NULL, point_size = 64,
ellipses = FALSE, transitions = TRUE, labels_positions = NULL)fviz_mca_var(res_mca, choice = "quanti.sup",
ggtheme = theme_minimal())
## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```
##
## ```
## drawn <-
## c("28", "24", "13", "21", "14", "42", "27", "39", "29", "41")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
##
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 4 clusters.*
##
##
## The 1st cluster is made of individuals such as *28*. This group is characterized by :
##
## - high frequency for factors like *MG_SOWSperworkeredit_57_113_147_=MG_SOWSperworkeredit_57_113_147__1*, *MG_BR_nopregus=MG_BR_nopregus_0*, *MG_FAR_far_assist_CAT=MG_FAR_far_assist_CAT_<6*, *MG_FAR_ind_0no_1rout_2sometimes=MG_FAR_ind_0no_1rout_2sometimes_0*, *MG_BR_rootny=MG_BR_rootny_1*, *MG_BR_bedny=MG_BR_bedny_1*, *MG_BR_feedtype=MG_BR_feedtype_2*, *MG_BR_artinspro_050_5099_100=MG_BR_artinspro_050_5099_100_0*, *MG_FAR_bedamount=MG_FAR_bedamount_1* and *MG_PR_feedtype=MG_PR_feedtype_2* (factors are sorted from the most common).
## - low frequency for the factors *MG_BR_feedtype=MG_BR_feedtype_4*, *MG_BR_bedny=MG_BR_bedny_0*, *MG_BR_rootny=MG_BR_rootny_0*, *MG_FAR_ox_0_13_46_7=MG_FAR_ox_0_13_46_7_3*, *MG_FAR_ind_0no_1rout_2sometimes=MG_FAR_ind_0no_1rout_2sometimes_2*, *MG_BR_nopregus=MG_BR_nopregus_1*, *MG_FAR_piglet_addfeedage=<7* and *MG_BR_artinspro_050_5099_100=MG_BR_artinspro_050_5099_100_2* (factors are sorted from the rarest).
##
## The cluster 2 is made of individuals such as*. This group is characterized by14* and *14*. :
##
## - high frequency for factors like *MG_PR_toy=MG_PR_toy_0*, *MG_PR_toyyn=MG_PR_toyyn_0*, *MG_FAR_toy=MG_FAR_toy_0*, *MG_PR_feedtype=MG_PR_feedtype_4*, *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_1*, *MG_PR_type=MG_PR_type_1*, *MG_FAR_bedamount=MG_FAR_bedamount_3*, *MG_rootamount=MG_rootamount_1*, *MG_BR_amount=MG_BR_amount_1* and *MG_PR_kuivaliete=MG_PR_kuivaliete_1* (factors are sorted from the most common).
## - low frequency for the factors *MG_PR_type=MG_PR_type_2*, *MG_PR_toyyn=MG_PR_toyyn_1*, *MG_FAR_toy=MG_FAR_toy_1*, *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_0*, *MG_PR_kuivaliete=MG_PR_kuivaliete_2*, *MG_PR_feedtype=MG_PR_feedtype_3*, *MG_PR_animdirtmed=MG_PR_animdirtmed_2*, *MG_SOWSperworkeredit_57_113_147_=MG_SOWSperworkeredit_57_113_147__4*, *MG_PR_feedtype=MG_PR_feedtype_2* and *MG_PR_toy=MG_PR_toy_4* (factors are sorted from the rarest).
##
## The cluster 3 is made of individuals such as*. This group is characterized by29* and *29*. :
##
## - high frequency for factors like *MG_SOWSperworkeredit_57_113_147_=MG_SOWSperworkeredit_57_113_147__4*, *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_4*, *MG_PR_type=MG_PR_type_2*, *MG_FAR_bed_yn=MG_FAR_bed_yn_0*, *MG_PR_toyyn=MG_PR_toyyn_1*, *MG_BR_ster=MG_BR_ster_1*, *MG_FAR_toy=MG_FAR_toy_1*, *MG_BR_bedny=MG_BR_bedny_0*, *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_1* and *MG_FAR_bedamount=MG_FAR_bedamount_4* (factors are sorted from the most common).
## - low frequency for factors like *MG_FAR_bed_yn=MG_FAR_bed_yn_1*, *MG_SOWSperworkeredit_57_113_147_=MG_SOWSperworkeredit_57_113_147__1*, *MG_PR_toyyn=MG_PR_toyyn_0*, *MG_PR_toy=MG_PR_toy_0*, *MG_BR_ster=MG_BR_ster_0*, *MG_FAR_toy=MG_FAR_toy_0*, *MG_FAR_bedamount=MG_FAR_bedamount_3*, *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_2*, *MG_BR_animdirtmed=MG_BR_animdirtmed_1* and *MG_BR_bedny=MG_BR_bedny_1* (factors are sorted from the rarest).
##
## The cluster 4 is made of individuals such as*. This group is characterized by13* and *13*. :
##
## - high frequency for factors like *MG_FAR_far_assist_CAT=MG_FAR_far_assist_CAT_noinfo*, *MG_rootamount=MG_rootamount_0*, *MG_PR_rootyn=MG_PR_rootyn_0*, *MG_BR_feedclean=MG_BR_feedclean_1*, *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_noinfo*, *MG_BR_triple_012=MG_BR_triple_012_noinfo*, *MG_BR_once_012=MG_BR_once_012_Not Assigned*, *MG_sickpen_yn=MG_sickpen_yn_0*, *MG_BR_amount=MG_BR_amount_4* and *MG_FAR_nestmatamount=MG_FAR_nestmatamount_0* (factors are sorted from the most common).
## - low frequency for the factors *MG_PR_rootyn=MG_PR_rootyn_1*, *MG_BR_feedclean=MG_BR_feedclean_0*, *MG_sickpen_yn=MG_sickpen_yn_1* and *MG_PR_animdirtmed=MG_PR_animdirtmed_1* (factors are sorted from the rarest).## **Results for the Hierarchical Clustering on Principal Components**
## name
## 1 "$data.clust"
## 2 "$desc.var"
## 3 "$desc.var$quanti.var"
## 4 "$desc.var$quanti"
## 5 "$desc.var$test.chi2"
## 6 "$desc.axes$category"
## 7 "$desc.axes"
## 8 "$desc.axes$quanti.var"
## 9 "$desc.axes$quanti"
## 10 "$desc.ind"
## 11 "$desc.ind$para"
## 12 "$desc.ind$dist"
## 13 "$call"
## 14 "$call$t"
## description
## 1 "dataset with the cluster of the individuals"
## 2 "description of the clusters by the variables"
## 3 "description of the cluster var. by the continuous var."
## 4 "description of the clusters by the continuous var."
## 5 "description of the cluster var. by the categorical var."
## 6 "description of the clusters by the categories."
## 7 "description of the clusters by the dimensions"
## 8 "description of the cluster var. by the axes"
## 9 "description of the clusters by the axes"
## 10 "description of the clusters by the individuals"
## 11 "parangons of each clusters"
## 12 "specific individuals"
## 13 "summary statistics"
## 14 "description of the tree"Housing; breeding unit
# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="roombr.csv", header=TRUE)glimpse(med)## Observations: 43
## Variables: 19
## $ R_BR_sowspersection <fctr> >100, all, all, 50-100, noinfo...
## $ R_BR_area2_NUM <fctr> 1,5275, group, 1,536, 1,5675, ...
## $ R_BR_type <int> 3, 1, 3, 3, 3, 1, 3, 3, 3, 3, 3...
## $ R_BR_noise <int> 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0...
## $ R_BR_pest_NO <int> 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0...
## $ R_BR_airqual <int> 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0...
## $ R_BR_C_NUM_NO <fctr> 19, 0, 18, 26, 19, 23, 19, 17,...
## $ R_BR_outC <fctr> , 8, 15, 25, 24, , , 2, 25, 25...
## $ R_BR_floorbetmetplastwoodother <fctr> bet, bet, bet, betmet, bet, be...
## $ R_BR_floorsolid_NUM_NO <int> 80, 80, 99, 80, 70, 100, 80, 90...
## $ R_BR_floorsolid_0981_2 <int> 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1...
## $ R_BR_kuivaliete <int> 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2...
## $ R_BR_PREGsame <int> 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1...
## $ OUT_SOW_mort_proNUM <int> 5, 5, 8, 27, 10, 0, 17, 13, 0, ...
## $ OUT_SOW_mort_dic <int> 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0...
## $ OUT_SOW_totremproNUM <int> 34, 38, 53, 57, 65, 64, 47, 44,...
## $ OUT_SOW_totrem_dic <int> 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0...
## $ OUT_SOW_cullproNUM <int> 29, 33, 45, 30, 55, 64, 30, 31,...
## $ OUT_SOW_cull_dic <int> 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0...med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM ## [1] 29 33 45 30 55 64 30 31 24 28 29 36 70 27 42 22 40
## [18] 29 38 38 27 30 44 45 34 45 41 35 49 32 39 80 14 24
## [35] 35 56 39 50 42 35 33 107 42
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107colnames(med)=="OUT_SOW_cullproNUM"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSEcolnames(med)=="OUT_SOWmortpro"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEmed$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))
X<-medmca
colnames(X)[ apply(X, 2, anyNA) ]## [1] "R_BR_PREGsame"for (i in 1:ncol(X)) {
levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}
X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)
medmca<-X #colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="lightgreen") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+ scale_fill_manual("key")
library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")
library(tableone)
KreateTableOne = function(x, ...){
t1 = tableone::CreateTableOne(data=x, ...)
t2 = print(t1, quote=TRUE)
rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
return(t2)
}Yhteenveto kuolleisuuden mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')table1%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 23 | 20 | ||
| R_BR_C_NUM_NO (mean (sd)) | 13.52 (7.56) | 15.80 (8.97) | 0.371 | |
| R_BR_floorsolid_NUM_NO (mean (sd)) | 7.87 (3.08) | 7.90 (2.02) | 0.970 | |
| R_BR_area2_NUM (mean (sd)) | 18.26 (9.99) | 16.10 (8.85) | 0.460 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 4.74 (2.12) | 13.35 (3.27) | <0.001 | |
| OUT_SOW_totremproNUM (mean (sd)) | 8.91 (5.80) | 17.85 (8.03) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 11.78 (6.69) | 15.35 (8.20) | 0.124 | |
| R_BR_sowspersection (%) | 0.509 | |||
| <20 | 2 ( 8.7) | 1 ( 5.0) | ||
| >100 | 3 (13.0) | 3 ( 15.0) | ||
| 20-50 | 5 (21.7) | 6 ( 30.0) | ||
| 50-100 | 9 (39.1) | 7 ( 35.0) | ||
| all | 4 (17.4) | 1 ( 5.0) | ||
| noinfo | 0 ( 0.0) | 2 ( 10.0) | ||
| R_BR_type (%) | 0.334 | |||
| 1 | 3 (13.0) | 1 ( 5.0) | ||
| 2 | 1 ( 4.3) | 1 ( 5.0) | ||
| 3 | 15 (65.2) | 16 ( 80.0) | ||
| 12 | 2 ( 8.7) | 0 ( 0.0) | ||
| 13 | 2 ( 8.7) | 0 ( 0.0) | ||
| 23 | 0 ( 0.0) | 1 ( 5.0) | ||
| 124 | 0 ( 0.0) | 1 ( 5.0) | ||
| R_BR_noise = 1 (%) | 13 (56.5) | 9 ( 45.0) | 0.654 | |
| R_BR_pest_NO = 1 (%) | 10 (43.5) | 11 ( 55.0) | 0.654 | |
| R_BR_airqual = 1 (%) | 7 (30.4) | 11 ( 55.0) | 0.187 | |
| R_BR_outC (%) | 0.374 | |||
| 13 (56.5) | 10 ( 50.0) | |||
| 10 | 1 ( 4.3) | 0 ( 0.0) | ||
| 13 | 1 ( 4.3) | 0 ( 0.0) | ||
| 14 | 0 ( 0.0) | 2 ( 10.0) | ||
| 15 | 1 ( 4.3) | 0 ( 0.0) | ||
| 18 | 1 ( 4.3) | 0 ( 0.0) | ||
| 19 | 1 ( 4.3) | 1 ( 5.0) | ||
| 2 | 0 ( 0.0) | 1 ( 5.0) | ||
| 20 | 0 ( 0.0) | 1 ( 5.0) | ||
| 23 | 0 ( 0.0) | 1 ( 5.0) | ||
| 24 | 0 ( 0.0) | 1 ( 5.0) | ||
| 25 | 4 (17.4) | 1 ( 5.0) | ||
| 28,3 | 0 ( 0.0) | 1 ( 5.0) | ||
| 31 | 0 ( 0.0) | 1 ( 5.0) | ||
| 8 | 1 ( 4.3) | 0 ( 0.0) | ||
| R_BR_floorbetmetplastwoodother = betmet (%) | 4 (17.4) | 8 ( 40.0) | 0.191 | |
| R_BR_floorsolid_0981_2 = 2 (%) | 5 (21.7) | 2 ( 10.0) | 0.531 | |
| R_BR_kuivaliete (%) | 0.769 | |||
| 1 | 6 (26.1) | 4 ( 20.0) | ||
| 2 | 15 (65.2) | 15 ( 75.0) | ||
| 12 | 2 ( 8.7) | 1 ( 5.0) | ||
| R_BR_PREGsame = 1 (%) | 8 (34.8) | 4 ( 21.1) | 0.524 | |
| OUT_SOW_mort_dic = 1 (%) | 0 ( 0.0) | 20 (100.0) | <0.001 | |
| OUT_SOW_totrem_dic = 1 (%) | 7 (30.4) | 15 ( 75.0) | 0.009 | |
| OUT_SOW_cull_dic = 1 (%) | 11 (47.8) | 10 ( 50.0) | 1.000 |
Yhteenveto poistojen mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')table2%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 22 | 21 | ||
| R_BR_C_NUM_NO (mean (sd)) | 15.50 (9.06) | 13.62 (7.33) | 0.460 | |
| R_BR_floorsolid_NUM_NO (mean (sd)) | 8.09 (2.62) | 7.67 (2.65) | 0.600 | |
| R_BR_area2_NUM (mean (sd)) | 18.41 (9.50) | 16.05 (9.44) | 0.418 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 8.73 (5.55) | 8.76 (4.73) | 0.983 | |
| OUT_SOW_totremproNUM (mean (sd)) | 7.77 (5.46) | 18.62 (6.82) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 7.18 (3.57) | 20.00 (4.27) | <0.001 | |
| R_BR_sowspersection (%) | 0.821 | |||
| <20 | 1 ( 4.5) | 2 ( 9.5) | ||
| >100 | 3 (13.6) | 3 ( 14.3) | ||
| 20-50 | 5 (22.7) | 6 ( 28.6) | ||
| 50-100 | 8 (36.4) | 8 ( 38.1) | ||
| all | 4 (18.2) | 1 ( 4.8) | ||
| noinfo | 1 ( 4.5) | 1 ( 4.8) | ||
| R_BR_type (%) | 0.542 | |||
| 1 | 1 ( 4.5) | 3 ( 14.3) | ||
| 2 | 2 ( 9.1) | 0 ( 0.0) | ||
| 3 | 16 (72.7) | 15 ( 71.4) | ||
| 12 | 1 ( 4.5) | 1 ( 4.8) | ||
| 13 | 1 ( 4.5) | 1 ( 4.8) | ||
| 23 | 0 ( 0.0) | 1 ( 4.8) | ||
| 124 | 1 ( 4.5) | 0 ( 0.0) | ||
| R_BR_noise = 1 (%) | 10 (45.5) | 12 ( 57.1) | 0.645 | |
| R_BR_pest_NO = 1 (%) | 11 (50.0) | 10 ( 47.6) | 1.000 | |
| R_BR_airqual = 1 (%) | 12 (54.5) | 6 ( 28.6) | 0.157 | |
| R_BR_outC (%) | 0.366 | |||
| 10 (45.5) | 13 ( 61.9) | |||
| 10 | 0 ( 0.0) | 1 ( 4.8) | ||
| 13 | 1 ( 4.5) | 0 ( 0.0) | ||
| 14 | 1 ( 4.5) | 1 ( 4.8) | ||
| 15 | 0 ( 0.0) | 1 ( 4.8) | ||
| 18 | 1 ( 4.5) | 0 ( 0.0) | ||
| 19 | 0 ( 0.0) | 2 ( 9.5) | ||
| 2 | 1 ( 4.5) | 0 ( 0.0) | ||
| 20 | 1 ( 4.5) | 0 ( 0.0) | ||
| 23 | 1 ( 4.5) | 0 ( 0.0) | ||
| 24 | 0 ( 0.0) | 1 ( 4.8) | ||
| 25 | 4 (18.2) | 1 ( 4.8) | ||
| 28,3 | 0 ( 0.0) | 1 ( 4.8) | ||
| 31 | 1 ( 4.5) | 0 ( 0.0) | ||
| 8 | 1 ( 4.5) | 0 ( 0.0) | ||
| R_BR_floorbetmetplastwoodother = betmet (%) | 6 (27.3) | 6 ( 28.6) | 1.000 | |
| R_BR_floorsolid_0981_2 = 2 (%) | 3 (13.6) | 4 ( 19.0) | 0.946 | |
| R_BR_kuivaliete (%) | 0.185 | |||
| 1 | 4 (18.2) | 6 ( 28.6) | ||
| 2 | 15 (68.2) | 15 ( 71.4) | ||
| 12 | 3 (13.6) | 0 ( 0.0) | ||
| R_BR_PREGsame = 1 (%) | 7 (33.3) | 5 ( 23.8) | 0.733 | |
| OUT_SOW_mort_dic = 1 (%) | 10 (45.5) | 10 ( 47.6) | 1.000 | |
| OUT_SOW_totrem_dic = 1 (%) | 5 (22.7) | 17 ( 81.0) | <0.001 | |
| OUT_SOW_cull_dic = 1 (%) | 0 ( 0.0) | 21 (100.0) | <0.001 |
Yhteenveto joku hylkays mukaan
res_mca = MCA(medmca, quanti.sup = c(13,14),quali.sup=c(11:12), graph = FALSE)summary(res_mca)##
## Call:
## MCA(X = medmca, quanti.sup = c(13, 14), quali.sup = c(11:12),
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6
## Variance 0.398 0.296 0.210 0.199 0.190 0.184
## % of var. 11.709 8.696 6.180 5.843 5.580 5.405
## Cumulative % of var. 11.709 20.405 26.585 32.428 38.007 43.413
## Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Dim.12
## Variance 0.169 0.155 0.139 0.132 0.128 0.121
## % of var. 4.958 4.554 4.083 3.880 3.756 3.570
## Cumulative % of var. 48.371 52.925 57.008 60.888 64.644 68.214
## Dim.13 Dim.14 Dim.15 Dim.16 Dim.17 Dim.18
## Variance 0.118 0.109 0.107 0.103 0.101 0.096
## % of var. 3.470 3.205 3.135 3.017 2.965 2.834
## Cumulative % of var. 71.684 74.889 78.024 81.040 84.005 86.840
## Dim.19 Dim.20 Dim.21 Dim.22 Dim.23 Dim.24
## Variance 0.087 0.075 0.055 0.052 0.045 0.034
## % of var. 2.545 2.215 1.631 1.539 1.310 0.995
## Cumulative % of var. 89.384 91.599 93.230 94.769 96.080 97.075
## Dim.25 Dim.26 Dim.27 Dim.28 Dim.29 Dim.30
## Variance 0.025 0.024 0.020 0.016 0.008 0.004
## % of var. 0.737 0.698 0.603 0.468 0.221 0.131
## Cumulative % of var. 97.811 98.509 99.112 99.580 99.801 99.932
## Dim.31 Dim.32 Dim.33 Dim.34
## Variance 0.002 0.000 0.000 0.000
## % of var. 0.054 0.014 0.000 0.000
## Cumulative % of var. 99.986 100.000 100.000 100.000
##
## Individuals (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | -0.234 0.319 0.045 | 0.021 0.004 0.000 |
## 2 | 0.977 5.574 0.144 | -0.119 0.111 0.002 |
## 3 | 1.357 10.752 0.287 | 0.326 0.836 0.017 |
## 4 | -0.601 2.113 0.214 | -0.104 0.085 0.006 |
## 5 | -0.700 2.863 0.072 | 1.340 14.132 0.265 |
## 6 | 1.172 8.019 0.498 | 0.066 0.034 0.002 |
## 7 | -0.374 0.818 0.132 | -0.335 0.882 0.105 |
## 8 | -0.524 1.605 0.056 | 0.172 0.232 0.006 |
## 9 | -0.009 0.000 0.000 | -0.347 0.945 0.071 |
## 10 | -0.435 1.104 0.093 | -0.482 1.824 0.114 |
## Dim.3 ctr cos2
## 1 0.123 0.168 0.012 |
## 2 -0.812 7.304 0.100 |
## 3 -0.655 4.755 0.067 |
## 4 -0.042 0.020 0.001 |
## 5 0.041 0.018 0.000 |
## 6 -0.024 0.006 0.000 |
## 7 -0.012 0.002 0.000 |
## 8 -0.044 0.021 0.000 |
## 9 0.062 0.043 0.002 |
## 10 0.050 0.027 0.001 |
##
## Categories (the 10 first)
## Dim.1 ctr cos2 v.test Dim.2 ctr cos2
## <20 | 1.796 5.654 0.242 3.188 | 0.405 0.386 0.012
## >100 | -0.439 0.675 0.031 -1.145 | -0.230 0.250 0.009
## 20-50 | -0.021 0.003 0.000 -0.080 | -0.379 1.246 0.050
## 50-100 | -0.469 2.054 0.130 -2.339 | -0.345 1.494 0.070
## all | 1.515 6.704 0.302 3.562 | 0.427 0.716 0.024
## noinfo | -1.299 1.973 0.082 -1.860 | 3.861 23.447 0.727
## R_BR_type_1 | 1.825 7.780 0.341 3.787 | 0.061 0.012 0.000
## R_BR_type_2 | 2.080 5.055 0.211 2.977 | 0.763 0.916 0.028
## R_BR_type_3 | -0.436 3.437 0.490 -4.538 | -0.051 0.064 0.007
## R_BR_type_12 | 0.882 0.909 0.038 1.263 | -0.203 0.065 0.002
## v.test Dim.3 ctr cos2 v.test
## <20 0.718 | 1.732 9.958 0.225 3.073 |
## >100 -0.600 | 0.349 0.811 0.020 0.912 |
## 20-50 -1.442 | 0.378 1.742 0.049 1.437 |
## 50-100 -1.719 | -0.283 1.420 0.048 -1.413 |
## all 1.003 | -1.352 10.119 0.241 -3.179 |
## noinfo 5.526 | -0.081 0.014 0.000 -0.115 |
## R_BR_type_1 0.127 | -0.402 0.717 0.017 -0.835 |
## R_BR_type_2 1.092 | 0.876 1.699 0.037 1.254 |
## R_BR_type_3 -0.535 | -0.101 0.348 0.026 -1.049 |
## R_BR_type_12 -0.290 | -1.255 3.487 0.077 -1.796 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## R_BR_sowspersection | 0.679 0.814 0.506 |
## R_BR_type | 0.702 0.040 0.488 |
## R_BR_noise | 0.038 0.237 0.115 |
## R_BR_airqual | 0.118 0.120 0.053 |
## R_BR_outC | 0.397 0.897 0.591 |
## R_BR_floorbetmetplastwoodother | 0.162 0.074 0.038 |
## R_BR_floorsolid_0981_2 | 0.718 0.029 0.005 |
## R_BR_kuivaliete | 0.613 0.013 0.270 |
## R_BR_PREGsame | 0.428 0.673 0.035 |
## OUT_SOW_mort_dic | 0.127 0.061 0.000 |
##
## Supplementary categories
## Dim.1 cos2 v.test Dim.2 cos2 v.test
## OUT_SOW_totrem_dic_0 | 0.234 0.052 1.485 | -0.118 0.013 -0.748 |
## OUT_SOW_totrem_dic_1 | -0.224 0.052 -1.485 | 0.113 0.013 0.748 |
## OUT_SOW_cull_dic_0 | -0.009 0.000 -0.062 | 0.122 0.016 0.808 |
## OUT_SOW_cull_dic_1 | 0.010 0.000 0.062 | -0.128 0.016 -0.808 |
## Dim.3 cos2 v.test
## OUT_SOW_totrem_dic_0 0.176 0.029 1.112 |
## OUT_SOW_totrem_dic_1 -0.168 0.029 -1.112 |
## OUT_SOW_cull_dic_0 0.059 0.004 0.394 |
## OUT_SOW_cull_dic_1 -0.062 0.004 -0.394 |
##
## Supplementary categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## OUT_SOW_totrem_dic | 0.052 0.013 0.029 |
## OUT_SOW_cull_dic | 0.000 0.016 0.004 |
##
## Supplementary continuous variables
## Dim.1 Dim.2 Dim.3
## OUT_SOW_mort_proNUM | -0.361 | 0.204 | -0.120 |
## OUT_SOW_cullproNUM | -0.024 | 0.008 | -0.016 |To visualize the percentage of inertia explained by each MCA dimension:
eig.val <- res_mca$eig
barplot(eig.val[, 2],
names.arg = 1:nrow(eig.val),
main = "Variances Explained by Dimensions (%)",
xlab = "Principal Dimensions",
ylab = "Percentage of variances",
col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2],
type = "b", pch = 19, col = "red")
res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
var_sup = TRUE, var_lab_min_contrib = 0,
col_var = "Variable", symbol_var = "Type",
size_var = NULL, size_range = c(10, 300),
labels_size = 10, point_size = 56,
transitions = TRUE, labels_positions = NULL)res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
lab_var = NULL, , ind_lab_min_contrib = 0,
col_var = NULL, labels_size = 9,
point_opacity = 0.5, opacity_var = NULL, point_size = 64,
ellipses = FALSE, transitions = TRUE, labels_positions = NULL)fviz_mca_var(res_mca, choice = "quanti.sup",
ggtheme = theme_minimal())
## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```
##
## ```
## drawn <-
## c("35", "6", "42", "27", "34", "39", "18", "5", "3", "12")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
##
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
##
##
## The cluster 1 is made of individuals such as*. This group is characterized by5* and *5*. :
##
## - high frequency for the factors *R_BR_sowspersection=noinfo*, *R_BR_PREGsame=R_BR_PREGsame_Not Assigned*, *R_BR_outC=R_BR_outC_24* and *R_BR_outC=R_BR_outC_20* (factors are sorted from the most common).
##
## The cluster 2 is made of individuals such as*. This group is characterized by12* and *12*. :
##
## - high frequency for the factors *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_1*, *R_BR_kuivaliete=R_BR_kuivaliete_2*, *R_BR_sowspersection=50-100*, *R_BR_type=R_BR_type_3*, *R_BR_PREGsame=R_BR_PREGsame_0*, *R_BR_floorbetmetplastwoodother=betmet* and *R_BR_noise=R_BR_noise_1* (factors are sorted from the most common).
## - low frequency for the factors *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_2*, *R_BR_kuivaliete=R_BR_kuivaliete_1*, *R_BR_sowspersection=all*, *R_BR_type=R_BR_type_1*, *R_BR_floorbetmetplastwoodother=bet*, *R_BR_PREGsame=R_BR_PREGsame_1*, *R_BR_sowspersection=<20* and *R_BR_noise=R_BR_noise_0* (factors are sorted from the rarest).
##
## The cluster 3 is made of individuals such as*. This group is characterized by3* and *3*. :
##
## - high frequency for the factors *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_2*, *R_BR_kuivaliete=R_BR_kuivaliete_1*, *R_BR_sowspersection=all*, *R_BR_type=R_BR_type_1*, *R_BR_PREGsame=R_BR_PREGsame_1*, *R_BR_sowspersection=<20*, *R_BR_floorbetmetplastwoodother=bet* and *R_BR_type=R_BR_type_2* (factors are sorted from the most common).
## - low frequency for the factors *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_1*, *R_BR_kuivaliete=R_BR_kuivaliete_2*, *R_BR_type=R_BR_type_3*, *R_BR_PREGsame=R_BR_PREGsame_0*, *R_BR_sowspersection=50-100* and *R_BR_floorbetmetplastwoodother=betmet* (factors are sorted from the rarest).## **Results for the Hierarchical Clustering on Principal Components**
## name
## 1 "$data.clust"
## 2 "$desc.var"
## 3 "$desc.var$test.chi2"
## 4 "$desc.axes$category"
## 5 "$desc.axes"
## 6 "$desc.axes$quanti.var"
## 7 "$desc.axes$quanti"
## 8 "$desc.ind"
## 9 "$desc.ind$para"
## 10 "$desc.ind$dist"
## 11 "$call"
## 12 "$call$t"
## description
## 1 "dataset with the cluster of the individuals"
## 2 "description of the clusters by the variables"
## 3 "description of the cluster var. by the categorical var."
## 4 "description of the clusters by the categories."
## 5 "description of the clusters by the dimensions"
## 6 "description of the cluster var. by the axes"
## 7 "description of the clusters by the axes"
## 8 "description of the clusters by the individuals"
## 9 "parangons of each clusters"
## 10 "specific individuals"
## 11 "summary statistics"
## 12 "description of the tree"Housing; gestation unit
# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="roompr.csv", header=TRUE)glimpse(med)## Observations: 43
## Variables: 23
## $ R_PR_sectionsNUM_NO <int> 6, 1, 1, 5, NA, 1, 1, 2, 1, 3, 1, 1, 6,...
## $ R_PR_sowsinsecNUM_NO <fctr> 48, 27, 200, 60, 365, 32, 120, 60, 66,...
## $ R_PR_sowsNUM_NO <fctr> 8, 27, 12, 20, 12, 6, 20, 7, 16, 36, 8...
## $ R_PR_areapersow_NUM_NO <fctr> 3,0, 3,1, 4,1, 2,6, 3,8, 4,4, 2,3, 5,6...
## $ R_PR_areaNUM <fctr> 3,0, 2,1, 4,1, 2,6, 3,8, , 2,3, 2,3, 3...
## $ R_PR_areapersow <int> 2, 2, 4, 1, 4, 4, 1, 4, 3, 3, 3, 2, 1, ...
## $ R_PR_crareaNUM_NO <fctr> , , 1,5, 1,9, 2,0, 0,0, 1,9, 1,6, 1,4,...
## $ R_PR_nonoise <int> 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, ...
## $ R_PR_air <int> 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, ...
## $ R_PR_CNUM_NO <int> 17, 19, 18, 25, 18, 23, 19, 12, 28, 25,...
## $ R_PR_floorsolidNUM_NO <int> 70, 80, 99, 40, 70, 100, 80, 100, 67, 1...
## $ R_PR_floorsolid_0791_2 <int> 1, 2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, ...
## $ R_PR_bedmatyn <int> 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, ...
## $ R_PR_bedmatamount <int> 0, 2, 1, 0, 0, 1, 0, 1, 3, 1, 2, 0, 0, ...
## $ R_PR_floornoslip <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...
## $ R_PR_dirtNUM_NO <int> 10, 0, 0, 20, NA, 0, 0, 0, 20, 20, 0, 1...
## $ R_PR_dirtmed <int> 1, 1, 1, 2, NA, 1, 1, 1, 2, 2, 1, 2, 2,...
## $ OUT_SOW_mort_proNUM <int> 5, 5, 8, 27, 10, 0, 17, 13, 0, 6, 9, 5,...
## $ OUT_SOW_mort_dic <int> 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, ...
## $ OUT_SOW_totremproNUM <int> 34, 38, 53, 57, 65, 64, 47, 44, 24, 34,...
## $ OUT_SOW_totrem_dic <int> 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, ...
## $ OUT_SOW_cullproNUM <int> 29, 33, 45, 30, 55, 64, 30, 31, 24, 28,...
## $ OUT_SOW_cull_dic <int> 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, ...med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM ## [1] 29 33 45 30 55 64 30 31 24 28 29 36 70 27 42 22 40
## [18] 29 38 38 27 30 44 45 34 45 41 35 49 32 39 80 14 24
## [35] 35 56 39 50 42 35 33 107 42
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107colnames(med)=="OUT_SOW_cullproNUM"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [23] FALSEcolnames(med)=="OUT_SOWmortpro"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSEmed$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))
X<-medmca
colnames(X)[ apply(X, 2, anyNA) ]## [1] "R_PR_dirtmed"for (i in 1:ncol(X)) {
levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}
X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)
medmca<-X #colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="green") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+ scale_fill_manual("key")
library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")
library(tableone)
KreateTableOne = function(x, ...){
t1 = tableone::CreateTableOne(data=x, ...)
t2 = print(t1, quote=TRUE)
rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
return(t2)
}Yhteenveto kuolleisuuden mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')table1%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 23 | 20 | ||
| R_PR_sectionsNUM_NO (mean (sd)) | 1.83 (1.44) | 2.16 (1.50) | 0.469 | |
| R_PR_sowsinsecNUM_NO (mean (sd)) | 17.74 (10.80) | 19.00 (8.99) | 0.682 | |
| R_PR_sowsNUM_NO (mean (sd)) | 9.61 (6.14) | 11.20 (5.62) | 0.383 | |
| R_PR_areapersow_NUM_NO (mean (sd)) | 11.91 (6.12) | 10.75 (6.95) | 0.563 | |
| R_PR_crareaNUM_NO (mean (sd)) | 6.39 (4.31) | 6.80 (5.26) | 0.781 | |
| R_PR_CNUM_NO (mean (sd)) | 6.43 (2.97) | 7.10 (3.70) | 0.517 | |
| R_PR_floorsolidNUM_NO (mean (sd)) | 10.61 (4.39) | 8.35 (4.16) | 0.092 | |
| R_PR_dirtNUM_NO (mean (sd)) | 2.76 (2.30) | 3.11 (2.11) | 0.627 | |
| R_PR_areaNUM (mean (sd)) | 10.48 (7.88) | 12.40 (6.16) | 0.383 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 4.74 (2.12) | 13.35 (3.27) | <0.001 | |
| OUT_SOW_totremproNUM (mean (sd)) | 8.91 (5.80) | 17.85 (8.03) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 11.78 (6.69) | 15.35 (8.20) | 0.124 | |
| R_PR_areapersow (%) | 0.593 | |||
| 1 | 4 (17.4) | 7 ( 35.0) | ||
| 2 | 8 (34.8) | 5 ( 25.0) | ||
| 3 | 5 (21.7) | 3 ( 15.0) | ||
| 4 | 6 (26.1) | 5 ( 25.0) | ||
| R_PR_nonoise = 1 (%) | 12 (52.2) | 12 ( 60.0) | 0.836 | |
| R_PR_air = 1 (%) | 2 ( 8.7) | 9 ( 45.0) | 0.018 | |
| R_PR_floorsolid_0791_2 = 2 (%) | 12 (52.2) | 5 ( 25.0) | 0.132 | |
| R_PR_bedmatyn = 1 (%) | 18 (78.3) | 12 ( 60.0) | 0.333 | |
| R_PR_bedmatamount (%) | 0.387 | |||
| 0 | 5 (21.7) | 8 ( 40.0) | ||
| 1 | 9 (39.1) | 4 ( 20.0) | ||
| 2 | 5 (21.7) | 3 ( 15.0) | ||
| 3 | 4 (17.4) | 5 ( 25.0) | ||
| R_PR_floornoslip = 1 (%) | 3 (13.0) | 2 ( 10.0) | 1.000 | |
| R_PR_dirtmed = 2 (%) | 9 (42.9) | 9 ( 50.0) | 0.901 | |
| OUT_SOW_mort_dic = 1 (%) | 0 ( 0.0) | 20 (100.0) | <0.001 | |
| OUT_SOW_totrem_dic = 1 (%) | 7 (30.4) | 15 ( 75.0) | 0.009 | |
| OUT_SOW_cull_dic = 1 (%) | 11 (47.8) | 10 ( 50.0) | 1.000 |
Yhteenveto poistojen mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')table2%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 22 | 21 | ||
| R_PR_sectionsNUM_NO (mean (sd)) | 2.00 (1.51) | 1.95 (1.43) | 0.913 | |
| R_PR_sowsinsecNUM_NO (mean (sd)) | 18.59 (10.13) | 18.05 (9.90) | 0.860 | |
| R_PR_sowsNUM_NO (mean (sd)) | 11.32 (5.75) | 9.33 (5.99) | 0.274 | |
| R_PR_areapersow_NUM_NO (mean (sd)) | 11.59 (6.02) | 11.14 (7.04) | 0.823 | |
| R_PR_crareaNUM_NO (mean (sd)) | 6.68 (4.52) | 6.48 (5.04) | 0.889 | |
| R_PR_CNUM_NO (mean (sd)) | 7.32 (3.98) | 6.14 (2.35) | 0.248 | |
| R_PR_floorsolidNUM_NO (mean (sd)) | 9.73 (4.48) | 9.38 (4.38) | 0.799 | |
| R_PR_dirtNUM_NO (mean (sd)) | 2.75 (2.31) | 3.11 (2.11) | 0.620 | |
| R_PR_areaNUM (mean (sd)) | 12.45 (6.72) | 10.24 (7.50) | 0.313 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 8.73 (5.55) | 8.76 (4.73) | 0.983 | |
| OUT_SOW_totremproNUM (mean (sd)) | 7.77 (5.46) | 18.62 (6.82) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 7.18 (3.57) | 20.00 (4.27) | <0.001 | |
| R_PR_areapersow (%) | 0.108 | |||
| 1 | 6 (27.3) | 5 ( 23.8) | ||
| 2 | 5 (22.7) | 8 ( 38.1) | ||
| 3 | 7 (31.8) | 1 ( 4.8) | ||
| 4 | 4 (18.2) | 7 ( 33.3) | ||
| R_PR_nonoise = 1 (%) | 11 (50.0) | 13 ( 61.9) | 0.632 | |
| R_PR_air = 1 (%) | 5 (22.7) | 6 ( 28.6) | 0.929 | |
| R_PR_floorsolid_0791_2 = 2 (%) | 10 (45.5) | 7 ( 33.3) | 0.617 | |
| R_PR_bedmatyn = 1 (%) | 16 (72.7) | 14 ( 66.7) | 0.920 | |
| R_PR_bedmatamount (%) | 0.863 | |||
| 0 | 6 (27.3) | 7 ( 33.3) | ||
| 1 | 6 (27.3) | 7 ( 33.3) | ||
| 2 | 5 (22.7) | 3 ( 14.3) | ||
| 3 | 5 (22.7) | 4 ( 19.0) | ||
| R_PR_floornoslip = 1 (%) | 1 ( 4.5) | 4 ( 19.0) | 0.314 | |
| R_PR_dirtmed = 2 (%) | 8 (40.0) | 10 ( 52.6) | 0.639 | |
| OUT_SOW_mort_dic = 1 (%) | 10 (45.5) | 10 ( 47.6) | 1.000 | |
| OUT_SOW_totrem_dic = 1 (%) | 5 (22.7) | 17 ( 81.0) | <0.001 | |
| OUT_SOW_cull_dic = 1 (%) | 0 ( 0.0) | 21 (100.0) | <0.001 |
Yhteenveto joku hylkays mukaan
res_mca = MCA(medmca, quanti.sup = c(12,13),quali.sup=c(10:11), graph = FALSE)summary(res_mca)##
## Call:
## MCA(X = medmca, quanti.sup = c(12, 13), quali.sup = c(10:11),
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6
## Variance 0.351 0.239 0.162 0.152 0.132 0.129
## % of var. 22.585 15.351 10.405 9.759 8.513 8.268
## Cumulative % of var. 22.585 37.936 48.341 58.100 66.613 74.881
## Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Dim.12
## Variance 0.094 0.074 0.064 0.053 0.048 0.038
## % of var. 6.069 4.768 4.097 3.422 3.087 2.468
## Cumulative % of var. 80.950 85.718 89.815 93.237 96.324 98.792
## Dim.13 Dim.14
## Variance 0.019 0.000
## % of var. 1.208 0.000
## Cumulative % of var. 100.000 100.000
##
## Individuals (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | 0.500 1.653 0.120 | 0.086 0.073 0.004 |
## 2 | -0.407 1.095 0.126 | -0.468 2.134 0.167 |
## 3 | -0.931 5.744 0.721 | 0.405 1.599 0.136 |
## 4 | 1.054 7.348 0.687 | 0.304 0.898 0.057 |
## 5 | 0.546 1.971 0.115 | 1.141 12.678 0.501 |
## 6 | -0.931 5.744 0.721 | 0.405 1.599 0.136 |
## 7 | 0.586 2.274 0.205 | 0.570 3.168 0.194 |
## 8 | -0.781 4.040 0.494 | 0.507 2.502 0.208 |
## 9 | -0.058 0.022 0.002 | -0.702 4.804 0.348 |
## 10 | -0.544 1.962 0.219 | -0.352 1.206 0.092 |
## Dim.3 ctr cos2
## 1 0.359 1.848 0.062 |
## 2 0.548 4.308 0.228 |
## 3 0.050 0.036 0.002 |
## 4 -0.001 0.000 0.000 |
## 5 -0.071 0.073 0.002 |
## 6 0.050 0.036 0.002 |
## 7 0.110 0.174 0.007 |
## 8 -0.093 0.124 0.007 |
## 9 -0.483 3.353 0.165 |
## 10 0.229 0.755 0.039 |
##
## Categories (the 10 first)
## Dim.1 ctr cos2 v.test Dim.2 ctr
## R_PR_areapersow_1 | 0.841 5.719 0.243 3.195 | -0.190 0.431
## R_PR_areapersow_2 | 0.410 1.611 0.073 1.751 | -0.318 1.424
## R_PR_areapersow_3 | -0.497 1.452 0.056 -1.539 | -0.515 2.292
## R_PR_areapersow_4 | -0.965 7.527 0.320 -3.665 | 0.941 10.529
## R_PR_nonoise_0 | -0.280 1.093 0.062 -1.612 | 0.466 4.473
## R_PR_nonoise_1 | 0.221 0.865 0.062 1.612 | -0.369 3.541
## R_PR_air_0 | -0.248 1.443 0.178 -2.737 | -0.266 2.459
## R_PR_air_1 | 0.720 4.197 0.178 2.737 | 0.775 7.152
## R_PR_floorsolid_0791_2_1 | 0.553 5.839 0.467 4.429 | -0.053 0.079
## R_PR_floorsolid_0791_2_2 | -0.845 8.931 0.467 -4.429 | 0.081 0.121
## cos2 v.test Dim.3 ctr cos2 v.test
## R_PR_areapersow_1 0.012 -0.723 | -0.663 7.726 0.151 -2.520 |
## R_PR_areapersow_2 0.044 -1.357 | 0.787 12.863 0.269 3.359 |
## R_PR_areapersow_3 0.061 -1.594 | -0.157 0.315 0.006 -0.487 |
## R_PR_areapersow_4 0.304 3.574 | -0.153 0.411 0.008 -0.581 |
## R_PR_nonoise_0 0.172 2.690 | -0.118 0.421 0.011 -0.680 |
## R_PR_nonoise_1 0.172 -2.690 | 0.093 0.334 0.011 0.680 |
## R_PR_air_0 0.207 -2.945 | -0.205 2.141 0.122 -2.263 |
## R_PR_air_1 0.207 2.945 | 0.595 6.227 0.122 2.263 |
## R_PR_floorsolid_0791_2_1 0.004 -0.425 | -0.334 4.629 0.171 -2.677 |
## R_PR_floorsolid_0791_2_2 0.004 0.425 | 0.511 7.080 0.171 2.677 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## R_PR_areapersow | 0.516 0.315 0.310 |
## R_PR_nonoise | 0.062 0.172 0.011 |
## R_PR_air | 0.178 0.207 0.122 |
## R_PR_floorsolid_0791_2 | 0.467 0.004 0.171 |
## R_PR_bedmatyn | 0.615 0.231 0.018 |
## R_PR_bedmatamount | 0.818 0.663 0.642 |
## R_PR_floornoslip | 0.073 0.151 0.064 |
## R_PR_dirtmed | 0.274 0.356 0.052 |
## OUT_SOW_mort_dic | 0.160 0.050 0.067 |
##
## Supplementary categories
## Dim.1 cos2 v.test Dim.2 cos2 v.test
## OUT_SOW_totrem_dic_0 | -0.511 0.249 -3.237 | -0.081 0.006 -0.512 |
## OUT_SOW_totrem_dic_1 | 0.488 0.249 3.237 | 0.077 0.006 0.512 |
## OUT_SOW_cull_dic_0 | -0.107 0.012 -0.712 | -0.040 0.002 -0.266 |
## OUT_SOW_cull_dic_1 | 0.113 0.012 0.712 | 0.042 0.002 0.266 |
## Dim.3 cos2 v.test
## OUT_SOW_totrem_dic_0 0.052 0.003 0.327 |
## OUT_SOW_totrem_dic_1 -0.049 0.003 -0.327 |
## OUT_SOW_cull_dic_0 -0.103 0.011 -0.685 |
## OUT_SOW_cull_dic_1 0.108 0.011 0.685 |
##
## Supplementary categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## OUT_SOW_totrem_dic | 0.249 0.006 0.003 |
## OUT_SOW_cull_dic | 0.012 0.002 0.011 |
##
## Supplementary continuous variables
## Dim.1 Dim.2 Dim.3
## OUT_SOW_mort_proNUM | 0.469 | 0.144 | -0.059 |
## OUT_SOW_cullproNUM | 0.175 | 0.103 | 0.046 |To visualize the percentage of inertia explained by each MCA dimension:
eig.val <- res_mca$eig
barplot(eig.val[, 2],
names.arg = 1:nrow(eig.val),
main = "Variances Explained by Dimensions (%)",
xlab = "Principal Dimensions",
ylab = "Percentage of variances",
col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2],
type = "b", pch = 19, col = "red")
res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
var_sup = TRUE, var_lab_min_contrib = 0,
col_var = "Variable", symbol_var = "Type",
size_var = NULL, size_range = c(10, 300),
labels_size = 10, point_size = 56,
transitions = TRUE, labels_positions = NULL)res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
lab_var = NULL, , ind_lab_min_contrib = 0,
col_var = NULL, labels_size = 9,
point_opacity = 0.5, opacity_var = NULL, point_size = 64,
ellipses = FALSE, transitions = TRUE, labels_positions = NULL)fviz_mca_var(res_mca, choice = "quanti.sup",
ggtheme = theme_minimal())
## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```
##
## ```
## drawn <-
## c("6", "20", "3", "4", "8", "40", "24", "27", "39", "5", "13",
## "19")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
##
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
##
##
## The cluster 1 is made of individuals such as*. This group is characterized by3* and *3*. :
##
## - high frequency for the factors *R_PR_bedmatamount=R_PR_bedmatamount_1*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_2*, *R_PR_areapersow=R_PR_areapersow_4*, *R_PR_bedmatyn=R_PR_bedmatyn_1*, *R_PR_dirtmed=R_PR_dirtmed_1* and *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_0* (factors are sorted from the most common).
## - low frequency for the factors *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_1*, *R_PR_bedmatyn=R_PR_bedmatyn_0*, *R_PR_bedmatamount=R_PR_bedmatamount_0*, *R_PR_areapersow=R_PR_areapersow_1*, *R_PR_dirtmed=R_PR_dirtmed_2*, *R_PR_bedmatamount=R_PR_bedmatamount_3* and *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_1* (factors are sorted from the rarest).
##
## The cluster 2 is made of individuals sharing :
##
## - high frequency for the factors *R_PR_bedmatamount=R_PR_bedmatamount_3*, *R_PR_bedmatyn=R_PR_bedmatyn_1*, *R_PR_air=R_PR_air_0*, *R_PR_bedmatamount=R_PR_bedmatamount_2* and *R_PR_dirtmed=R_PR_dirtmed_2* (factors are sorted from the most common).
## - low frequency for the factors *R_PR_bedmatyn=R_PR_bedmatyn_0*, *R_PR_bedmatamount=R_PR_bedmatamount_0*, *R_PR_bedmatamount=R_PR_bedmatamount_1*, *R_PR_air=R_PR_air_1* and *R_PR_areapersow=R_PR_areapersow_4* (factors are sorted from the rarest).
##
## The cluster 3 is made of individuals such as*. This group is characterized by4* and *4*. :
##
## - high frequency for the factors *R_PR_bedmatamount=R_PR_bedmatamount_0*, *R_PR_bedmatyn=R_PR_bedmatyn_0*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_1*, *R_PR_air=R_PR_air_1* and *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_1* (factors are sorted from the most common).
## - low frequency for the factors *R_PR_bedmatyn=R_PR_bedmatyn_1*, *R_PR_bedmatamount=R_PR_bedmatamount_1*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_2*, *R_PR_air=R_PR_air_0*, *R_PR_bedmatamount=R_PR_bedmatamount_3*, *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_0* and *R_PR_bedmatamount=R_PR_bedmatamount_2* (factors are sorted from the rarest).## **Results for the Hierarchical Clustering on Principal Components**
## name
## 1 "$data.clust"
## 2 "$desc.var"
## 3 "$desc.var$test.chi2"
## 4 "$desc.axes$category"
## 5 "$desc.axes"
## 6 "$desc.axes$quanti.var"
## 7 "$desc.axes$quanti"
## 8 "$desc.ind"
## 9 "$desc.ind$para"
## 10 "$desc.ind$dist"
## 11 "$call"
## 12 "$call$t"
## description
## 1 "dataset with the cluster of the individuals"
## 2 "description of the clusters by the variables"
## 3 "description of the cluster var. by the categorical var."
## 4 "description of the clusters by the categories."
## 5 "description of the clusters by the dimensions"
## 6 "description of the cluster var. by the axes"
## 7 "description of the clusters by the axes"
## 8 "description of the clusters by the individuals"
## 9 "parangons of each clusters"
## 10 "specific individuals"
## 11 "summary statistics"
## 12 "description of the tree"Housing; farrowing unit
# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="roomfar.csv", header=TRUE)glimpse(med)## Observations: 43
## Variables: 18
## $ R_FAR_secNUM_NO <int> 6, 1, 4, 4, 12, 2, 4, 2, ...
## $ R_FAR_pensinsecNUM_NO <int> 28, 10, 15, 40, 24, 18, 1...
## $ R_FARpenNUM_NO <fctr> 4,8, 4,6, 5,6, 3,9, , 8,...
## $ R_FAR_pensize_med4.9 <fctr> 1,0, 1,0, 2,0, 1,0, , 2,...
## $ R_FAR_noise <int> 1, 1, 0, 1, 0, 1, 0, 1, 0...
## $ R_FAR_airqual <int> 1, 0, 0, 0, 0, 0, 1, 0, 0...
## $ R_FAR_C_NUM_NO <int> 19, 18, 24, 26, 23, 26, 2...
## $ R_FAR_floorsolidNUM_NO <int> 0, 100, 50, 60, 10, 100, ...
## $ R_FAR_floorsolid_all0_100_100_2_muu1 <int> 0, 2, 1, 1, 0, 2, 0, 1, 1...
## $ R_FAR_kuivaliete <int> 2, 1, 1, 2, 2, 1, 2, 2, 2...
## $ R_FAR_kunto <int> 0, 0, 0, 0, 0, 0, 0, 0, 0...
## $ R_FAR_nonslippery <int> 0, 0, 0, 0, 0, 0, 1, 0, 0...
## $ OUT_SOW_mort_proNUM <int> 5, 5, 8, 27, 10, 0, 17, 1...
## $ OUT_SOW_mort_dic <int> 0, 0, 0, 1, 1, 0, 1, 1, 0...
## $ OUT_SOW_totremproNUM <int> 34, 38, 53, 57, 65, 64, 4...
## $ OUT_SOW_totrem_dic <int> 0, 0, 1, 1, 1, 1, 1, 0, 0...
## $ OUT_SOW_cullproNUM <int> 29, 33, 45, 30, 55, 64, 3...
## $ OUT_SOW_cull_dic <int> 0, 0, 1, 0, 1, 1, 0, 0, 0...med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM ## [1] 29 33 45 30 55 64 30 31 24 28 29 36 70 27 42 22 40
## [18] 29 38 38 27 30 44 45 34 45 41 35 49 32 39 80 14 24
## [35] 35 56 39 50 42 35 33 107 42
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107colnames(med)=="OUT_SOW_cullproNUM"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE TRUE FALSEcolnames(med)=="OUT_SOWmortpro"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSEmed$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))
X<-medmca
colnames(X)[ apply(X, 2, anyNA) ]## character(0)for (i in 1:ncol(X)) {
levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}
X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)
medmca<-X #colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="darkgreen") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+ scale_fill_manual("key")
library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")
library(tableone)
KreateTableOne = function(x, ...){
t1 = tableone::CreateTableOne(data=x, ...)
t2 = print(t1, quote=TRUE)
rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
return(t2)
}Yhteenveto kuolleisuuden mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')table1%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 23 | 20 | ||
| R_FAR_secNUM_NO (mean (sd)) | 3.22 (2.17) | 4.15 (2.80) | 0.226 | |
| R_FAR_pensinsecNUM_NO (mean (sd)) | 8.43 (4.90) | 12.10 (5.16) | 0.022 | |
| R_FARpenNUM_NO (mean (sd)) | 12.26 (5.99) | 8.65 (4.57) | 0.034 | |
| R_FAR_C_NUM_NO (mean (sd)) | 5.09 (2.27) | 5.55 (2.80) | 0.553 | |
| R_FAR_floorsolidNUM_NO (mean (sd)) | 6.04 (3.65) | 6.75 (3.21) | 0.507 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 4.74 (2.12) | 13.35 (3.27) | <0.001 | |
| OUT_SOW_totremproNUM (mean (sd)) | 8.91 (5.80) | 17.85 (8.03) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 11.78 (6.69) | 15.35 (8.20) | 0.124 | |
| R_FAR_pensize_med4.9 (%) | 0.340 | |||
| 0 ( 0.0) | 1 ( 5.0) | |||
| 1,0 | 11 (47.8) | 12 ( 60.0) | ||
| 2,0 | 12 (52.2) | 7 ( 35.0) | ||
| R_FAR_noise = 1 (%) | 11 (47.8) | 8 ( 40.0) | 0.836 | |
| R_FAR_airqual = 1 (%) | 2 ( 8.7) | 2 ( 10.0) | 1.000 | |
| R_FAR_floorsolid_all0_100_100_2_muu1 (%) | 0.132 | |||
| 0 | 6 (26.1) | 5 ( 25.0) | ||
| 1 | 13 (56.5) | 15 ( 75.0) | ||
| 2 | 4 (17.4) | 0 ( 0.0) | ||
| R_FAR_kuivaliete = 2 (%) | 17 (73.9) | 19 ( 95.0) | 0.146 | |
| R_FAR_kunto = 1 (%) | 1 ( 4.3) | 3 ( 15.0) | 0.501 | |
| R_FAR_nonslippery = 1 (%) | 5 (21.7) | 2 ( 10.0) | 0.531 | |
| OUT_SOW_mort_dic = 1 (%) | 0 ( 0.0) | 20 (100.0) | <0.001 | |
| OUT_SOW_totrem_dic = 1 (%) | 7 (30.4) | 15 ( 75.0) | 0.009 | |
| OUT_SOW_cull_dic = 1 (%) | 11 (47.8) | 10 ( 50.0) | 1.000 |
Yhteenveto poistojen mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')table2%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 22 | 21 | ||
| R_FAR_secNUM_NO (mean (sd)) | 3.00 (2.23) | 4.33 (2.63) | 0.080 | |
| R_FAR_pensinsecNUM_NO (mean (sd)) | 9.36 (4.30) | 10.95 (6.17) | 0.331 | |
| R_FARpenNUM_NO (mean (sd)) | 10.45 (5.03) | 10.71 (6.29) | 0.882 | |
| R_FAR_C_NUM_NO (mean (sd)) | 5.23 (2.86) | 5.38 (2.16) | 0.844 | |
| R_FAR_floorsolidNUM_NO (mean (sd)) | 6.82 (3.54) | 5.90 (3.33) | 0.389 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 8.73 (5.55) | 8.76 (4.73) | 0.983 | |
| OUT_SOW_totremproNUM (mean (sd)) | 7.77 (5.46) | 18.62 (6.82) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 7.18 (3.57) | 20.00 (4.27) | <0.001 | |
| R_FAR_pensize_med4.9 (%) | 0.491 | |||
| 0 ( 0.0) | 1 ( 4.8) | |||
| 1,0 | 13 (59.1) | 10 ( 47.6) | ||
| 2,0 | 9 (40.9) | 10 ( 47.6) | ||
| R_FAR_noise = 1 (%) | 12 (54.5) | 7 ( 33.3) | 0.274 | |
| R_FAR_airqual = 1 (%) | 2 ( 9.1) | 2 ( 9.5) | 1.000 | |
| R_FAR_floorsolid_all0_100_100_2_muu1 (%) | 0.379 | |||
| 0 | 4 (18.2) | 7 ( 33.3) | ||
| 1 | 15 (68.2) | 13 ( 61.9) | ||
| 2 | 3 (13.6) | 1 ( 4.8) | ||
| R_FAR_kuivaliete = 2 (%) | 19 (86.4) | 17 ( 81.0) | 0.946 | |
| R_FAR_kunto = 1 (%) | 1 ( 4.5) | 3 ( 14.3) | 0.566 | |
| R_FAR_nonslippery = 1 (%) | 4 (18.2) | 3 ( 14.3) | 1.000 | |
| OUT_SOW_mort_dic = 1 (%) | 10 (45.5) | 10 ( 47.6) | 1.000 | |
| OUT_SOW_totrem_dic = 1 (%) | 5 (22.7) | 17 ( 81.0) | <0.001 | |
| OUT_SOW_cull_dic = 1 (%) | 0 ( 0.0) | 21 (100.0) | <0.001 |
Yhteenveto joku hylkays mukaan
res_mca = MCA(medmca, quanti.sup = c(11,12),quali.sup=c(9:10), graph = FALSE)summary(res_mca)##
## Call:
## MCA(X = medmca, quanti.sup = c(11, 12), quali.sup = c(9:10),
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6
## Variance 0.269 0.214 0.167 0.154 0.115 0.106
## % of var. 21.558 17.115 13.324 12.314 9.233 8.504
## Cumulative % of var. 21.558 38.673 51.997 64.311 73.544 82.048
## Dim.7 Dim.8 Dim.9 Dim.10
## Variance 0.081 0.072 0.047 0.024
## % of var. 6.452 5.787 3.765 1.947
## Cumulative % of var. 88.500 94.288 98.053 100.000
##
## Individuals (the 10 first)
## Dim.1 ctr cos2 Dim.2
## 1 | -0.493 2.099 0.120 | 1.008
## 2 | 1.095 10.346 0.524 | 0.549
## 3 | 0.756 4.939 0.509 | -0.009
## 4 | -0.275 0.654 0.137 | -0.336
## 5 | -0.577 2.878 0.056 | 0.341
## 6 | 1.358 15.911 0.789 | 0.364
## 7 | -0.877 6.638 0.294 | 1.058
## 8 | -0.012 0.001 0.000 | -0.521
## 9 | -0.023 0.004 0.001 | -0.113
## 10 | 0.223 0.428 0.088 | -0.348
## ctr cos2 Dim.3 ctr
## 1 11.034 0.503 | -0.185 0.476
## 2 3.272 0.132 | -0.087 0.106
## 3 0.001 0.000 | 0.143 0.285
## 4 1.230 0.205 | -0.083 0.096
## 5 1.262 0.020 | 1.482 30.684
## 6 1.440 0.057 | 0.012 0.002
## 7 12.173 0.428 | -0.127 0.224
## 8 2.951 0.452 | 0.017 0.004
## 9 0.139 0.028 | -0.124 0.216
## 10 1.317 0.214 | -0.287 1.149
## cos2
## 1 0.017 |
## 2 0.003 |
## 3 0.018 |
## 4 0.012 |
## 5 0.371 |
## 6 0.000 |
## 7 0.006 |
## 8 0.000 |
## 9 0.034 |
## 10 0.145 |
##
## Categories (the 10 first)
## Dim.1 ctr cos2 v.test
## V1 | -1.112 1.335 0.029 -1.112 |
## 1,0 | -0.467 5.422 0.251 -3.249 |
## 2,0 | 0.624 7.992 0.309 3.601 |
## R_FAR_noise_0 | 0.032 0.027 0.001 0.235 |
## R_FAR_noise_1 | -0.041 0.034 0.001 -0.235 |
## R_FAR_airqual_0 | 0.111 0.523 0.121 2.255 |
## R_FAR_airqual_1 | -1.087 5.095 0.121 -2.255 |
## R_FAR_floorsolid_all0_100_100_2_muu1_0 | -0.748 6.632 0.192 -2.841 |
## R_FAR_floorsolid_all0_100_100_2_muu1_1 | -0.064 0.125 0.008 -0.569 |
## R_FAR_floorsolid_all0_100_100_2_muu1_2 | 2.506 27.097 0.644 5.201 |
## Dim.2 ctr cos2 v.test
## V1 0.737 0.737 0.013 0.737 |
## 1,0 0.292 2.658 0.098 2.027 |
## 2,0 -0.392 3.963 0.122 -2.259 |
## R_FAR_noise_0 0.082 0.219 0.008 0.597 |
## R_FAR_noise_1 -0.104 0.277 0.008 -0.597 |
## R_FAR_airqual_0 -0.229 2.770 0.510 -4.626 |
## R_FAR_airqual_1 2.229 27.007 0.510 4.626 |
## R_FAR_floorsolid_all0_100_100_2_muu1_0 1.249 23.334 0.537 4.748 |
## R_FAR_floorsolid_all0_100_100_2_muu1_1 -0.625 14.875 0.730 -5.536 |
## R_FAR_floorsolid_all0_100_100_2_muu1_2 0.941 4.812 0.091 1.953 |
## Dim.3 ctr cos2 v.test
## V1 3.632 23.030 0.314 3.632 |
## 1,0 -0.234 2.191 0.063 -1.624 |
## 2,0 0.092 0.278 0.007 0.528 |
## R_FAR_noise_0 0.378 5.993 0.181 2.755 |
## R_FAR_noise_1 -0.478 7.570 0.181 -2.755 |
## R_FAR_airqual_0 -0.025 0.043 0.006 -0.510 |
## R_FAR_airqual_1 0.246 0.422 0.006 0.510 |
## R_FAR_floorsolid_all0_100_100_2_muu1_0 0.249 1.189 0.021 0.945 |
## R_FAR_floorsolid_all0_100_100_2_muu1_1 -0.139 0.948 0.036 -1.233 |
## R_FAR_floorsolid_all0_100_100_2_muu1_2 0.290 0.589 0.009 0.603 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## R_FAR_pensize_med4.9 | 0.318 0.126 0.340 |
## R_FAR_noise | 0.001 0.008 0.181 |
## R_FAR_airqual | 0.121 0.510 0.006 |
## R_FAR_floorsolid_all0_100_100_2_muu1 | 0.730 0.736 0.036 |
## R_FAR_kuivaliete | 0.626 0.156 0.041 |
## R_FAR_kunto | 0.057 0.017 0.109 |
## R_FAR_nonslippery | 0.065 0.056 0.375 |
## OUT_SOW_mort_dic | 0.237 0.102 0.245 |
##
## Supplementary categories
## Dim.1 cos2 v.test Dim.2
## OUT_SOW_totrem_dic_0 | 0.290 0.080 1.835 | -0.014
## OUT_SOW_totrem_dic_1 | -0.277 0.080 -1.835 | 0.014
## OUT_SOW_cull_dic_0 | 0.054 0.003 0.356 | -0.041
## OUT_SOW_cull_dic_1 | -0.056 0.003 -0.356 | 0.043
## cos2 v.test Dim.3 cos2
## OUT_SOW_totrem_dic_0 0.000 -0.090 | -0.343 0.112
## OUT_SOW_totrem_dic_1 0.000 0.090 | 0.328 0.112
## OUT_SOW_cull_dic_0 0.002 -0.275 | -0.233 0.057
## OUT_SOW_cull_dic_1 0.002 0.275 | 0.244 0.057
## v.test
## OUT_SOW_totrem_dic_0 -2.172 |
## OUT_SOW_totrem_dic_1 2.172 |
## OUT_SOW_cull_dic_0 -1.546 |
## OUT_SOW_cull_dic_1 1.546 |
##
## Supplementary categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## OUT_SOW_totrem_dic | 0.080 0.000 0.112 |
## OUT_SOW_cull_dic | 0.003 0.002 0.057 |
##
## Supplementary continuous variables
## Dim.1 Dim.2 Dim.3
## OUT_SOW_mort_proNUM | -0.461 | -0.243 | 0.359 |
## OUT_SOW_cullproNUM | -0.129 | -0.033 | 0.253 |To visualize the percentage of inertia explained by each MCA dimension:
eig.val <- res_mca$eig
barplot(eig.val[, 2],
names.arg = 1:nrow(eig.val),
main = "Variances Explained by Dimensions (%)",
xlab = "Principal Dimensions",
ylab = "Percentage of variances",
col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2],
type = "b", pch = 19, col = "red")
res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
var_sup = TRUE, var_lab_min_contrib = 0,
col_var = "Variable", symbol_var = "Type",
size_var = NULL, size_range = c(10, 300),
labels_size = 10, point_size = 56,
transitions = TRUE, labels_positions = NULL)res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
lab_var = NULL, , ind_lab_min_contrib = 0,
col_var = NULL, labels_size = 9,
point_opacity = 0.5, opacity_var = NULL, point_size = 64,
ellipses = FALSE, transitions = TRUE, labels_positions = NULL)fviz_mca_var(res_mca, choice = "quanti.sup",
ggtheme = theme_minimal())
## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```
##
## ```
## drawn <-
## c("14", "18", "6", "7", "2", "1", "43", "20", "3", "8")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
##
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
##
##
## The cluster 1 is made of individuals such as*. This group is characterized by1* and *1*. :
##
## - high frequency for the factors *R_FAR_airqual=R_FAR_airqual_1* and *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_0* (factors are sorted from the most common).
## - low frequency for the factors *R_FAR_airqual=R_FAR_airqual_0* and *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_1* (factors are sorted from the rarest).
##
## The 1st cluster is made of individuals such as *8*. This group is characterized by :
##
## - high frequency for the factors *R_FAR_kuivaliete=R_FAR_kuivaliete_2*, *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_1* and *R_FAR_airqual=R_FAR_airqual_0* (factors are sorted from the most common).
## - low frequency for the factors *R_FAR_kuivaliete=R_FAR_kuivaliete_1*, *R_FAR_airqual=R_FAR_airqual_1* and *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_2* (factors are sorted from the rarest).
##
## The cluster 3 is made of individuals such as*. This group is characterized by2* and *2*. :
##
## - high frequency for the factors *R_FAR_kuivaliete=R_FAR_kuivaliete_1*, *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_2* and *OUT_SOW_mort_dic=OUT_SOW_mort_dic_0* (factors are sorted from the most common).
## - low frequency for the factors *R_FAR_kuivaliete=R_FAR_kuivaliete_2* and *OUT_SOW_mort_dic=OUT_SOW_mort_dic_1* (factors are sorted from the rarest).## **Results for the Hierarchical Clustering on Principal Components**
## name
## 1 "$data.clust"
## 2 "$desc.var"
## 3 "$desc.var$test.chi2"
## 4 "$desc.axes$category"
## 5 "$desc.axes"
## 6 "$desc.axes$quanti.var"
## 7 "$desc.axes$quanti"
## 8 "$desc.ind"
## 9 "$desc.ind$para"
## 10 "$desc.ind$dist"
## 11 "$call"
## 12 "$call$t"
## description
## 1 "dataset with the cluster of the individuals"
## 2 "description of the clusters by the variables"
## 3 "description of the cluster var. by the categorical var."
## 4 "description of the clusters by the categories."
## 5 "description of the clusters by the dimensions"
## 6 "description of the cluster var. by the axes"
## 7 "description of the clusters by the axes"
## 8 "description of the clusters by the individuals"
## 9 "parangons of each clusters"
## 10 "specific individuals"
## 11 "summary statistics"
## 12 "description of the tree"Housing
# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="room.csv", header=TRUE)glimpse(med)## Observations: 43
## Variables: 48
## $ R_BR_sowspersection <fctr> >100, all, all, 50-100, ...
## $ R_BR_area2_NUM <fctr> 1,5275, group, 1,536, 1,...
## $ R_BR_type <int> 3, 1, 3, 3, 3, 1, 3, 3, 3...
## $ R_BR_noise <int> 0, 1, 0, 0, 0, 0, 1, 0, 1...
## $ R_BR_pest_NO <int> 0, 1, 1, 0, 0, 0, 1, 1, 0...
## $ R_BR_airqual <int> 1, 0, 0, 1, 1, 0, 0, 1, 0...
## $ R_BR_C_NUM_NO <fctr> 19, 0, 18, 26, 19, 23, 1...
## $ R_BR_outC <fctr> , 8, 15, 25, 24, , , 2, ...
## $ R_BR_floorbetmetplastwoodother <fctr> bet, bet, bet, betmet, b...
## $ R_BR_floorsolid_NUM_NO <int> 80, 80, 99, 80, 70, 100, ...
## $ R_BR_floorsolid_0981_2 <int> 1, 1, 2, 1, 1, 2, 1, 1, 1...
## $ R_BR_kuivaliete <int> 2, 1, 1, 2, 2, 1, 2, 2, 2...
## $ R_BR_PREGsame <int> 0, 0, 1, 0, 0, 1, 0, 0, 1...
## $ R_PR_sectionsNUM_NO <int> 6, 1, 1, 5, NA, 1, 1, 2, ...
## $ R_PR_sowsinsecNUM_NO <fctr> 48, 27, 200, 60, 365, 32...
## $ R_PR_sowsNUM_NO <fctr> 8, 27, 12, 20, 12, 6, 20...
## $ R_PR_areapersow_NUM_NO <fctr> 3,0, 3,1, 4,1, 2,6, 3,8,...
## $ R_PR_areaNUM <fctr> 3,0, 2,1, 4,1, 2,6, 3,8,...
## $ R_PR_areapersow <int> 2, 2, 4, 1, 4, 4, 1, 4, 3...
## $ R_PR_crareaNUM_NO <fctr> , , 1,5, 1,9, 2,0, 0,0, ...
## $ R_PR_nonoise <int> 0, 1, 0, 1, 0, 0, 1, 0, 1...
## $ R_PR_air <int> 0, 0, 0, 1, 1, 0, 1, 0, 0...
## $ R_PR_CNUM_NO <int> 17, 19, 18, 25, 18, 23, 1...
## $ R_PR_floorsolidNUM_NO <int> 70, 80, 99, 40, 70, 100, ...
## $ R_PR_floorsolid_0791_2 <int> 1, 2, 2, 1, 1, 2, 2, 2, 1...
## $ R_PR_bedmatyn <int> 0, 1, 1, 0, 0, 1, 0, 1, 1...
## $ R_PR_bedmatamount <int> 0, 2, 1, 0, 0, 1, 0, 1, 3...
## $ R_PR_floornoslip <int> 1, 0, 0, 0, 0, 0, 0, 0, 0...
## $ R_PR_dirtNUM_NO <int> 10, 0, 0, 20, NA, 0, 0, 0...
## $ R_PR_dirtmed <int> 1, 1, 1, 2, NA, 1, 1, 1, ...
## $ R_FAR_secNUM_NO <int> 6, 1, 4, 4, 12, 2, 4, 2, ...
## $ R_FAR_pensinsecNUM_NO <int> 28, 10, 15, 40, 24, 18, 1...
## $ R_FARpenNUM_NO <fctr> 4,8, 4,6, 5,6, 3,9, , 8,...
## $ R_FAR_pensize_med4.9 <fctr> 1,0, 1,0, 2,0, 1,0, , 2,...
## $ R_FAR_noise <int> 1, 1, 0, 1, 0, 1, 0, 1, 0...
## $ R_FAR_airqual <int> 1, 0, 0, 0, 0, 0, 1, 0, 0...
## $ R_FAR_C_NUM_NO <int> 19, 18, 24, 26, 23, 26, 2...
## $ R_FAR_floorsolidNUM_NO <int> 0, 100, 50, 60, 10, 100, ...
## $ R_FAR_floorsolid_all0_100_100_2_muu1 <int> 0, 2, 1, 1, 0, 2, 0, 1, 1...
## $ R_FAR_kuivaliete <int> 2, 1, 1, 2, 2, 1, 2, 2, 2...
## $ R_FAR_kunto <int> 0, 0, 0, 0, 0, 0, 0, 0, 0...
## $ R_FAR_nonslippery <int> 0, 0, 0, 0, 0, 0, 1, 0, 0...
## $ OUT_SOW_mort_proNUM <int> 5, 5, 8, 27, 10, 0, 17, 1...
## $ OUT_SOW_mort_dic <int> 0, 0, 0, 1, 1, 0, 1, 1, 0...
## $ OUT_SOW_totremproNUM <int> 34, 38, 53, 57, 65, 64, 4...
## $ OUT_SOW_totrem_dic <int> 0, 0, 1, 1, 1, 1, 1, 0, 0...
## $ OUT_SOW_cullproNUM <int> 29, 33, 45, 30, 55, 64, 3...
## $ OUT_SOW_cull_dic <int> 0, 0, 1, 0, 1, 1, 0, 0, 0...med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM ## [1] 29 33 45 30 55 64 30 31 24 28 29 36 70 27 42 22 40
## [18] 29 38 38 27 30 44 45 34 45 41 35 49 32 39 80 14 24
## [35] 35 56 39 50 42 35 33 107 42
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107colnames(med)=="OUT_SOW_cullproNUM"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE TRUE FALSEcolnames(med)=="OUT_SOWmortpro"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSEmed$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("R_BR_outC")))
X<-medmca
colnames(X)[ apply(X, 2, anyNA) ]## [1] "R_BR_PREGsame" "R_PR_dirtmed"for (i in 1:ncol(X)) {
levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}
X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)
medmca<-X #colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="orange") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+ scale_fill_manual("key")
library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")
library(tableone)
KreateTableOne = function(x, ...){
t1 = tableone::CreateTableOne(data=x, ...)
t2 = print(t1, quote=TRUE)
rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
return(t2)
}Yhteenveto kuolleisuuden mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')table1%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 23 | 20 | ||
| R_BR_C_NUM_NO (mean (sd)) | 13.52 (7.56) | 15.80 (8.97) | 0.371 | |
| R_BR_floorsolid_NUM_NO (mean (sd)) | 7.87 (3.08) | 7.90 (2.02) | 0.970 | |
| R_PR_sectionsNUM_NO (mean (sd)) | 1.83 (1.44) | 2.16 (1.50) | 0.469 | |
| R_PR_sowsinsecNUM_NO (mean (sd)) | 17.74 (10.80) | 19.00 (8.99) | 0.682 | |
| R_PR_sowsNUM_NO (mean (sd)) | 9.61 (6.14) | 11.20 (5.62) | 0.383 | |
| R_PR_areapersow_NUM_NO (mean (sd)) | 11.91 (6.12) | 10.75 (6.95) | 0.563 | |
| R_PR_crareaNUM_NO (mean (sd)) | 6.39 (4.31) | 6.80 (5.26) | 0.781 | |
| R_PR_CNUM_NO (mean (sd)) | 6.43 (2.97) | 7.10 (3.70) | 0.517 | |
| R_PR_floorsolidNUM_NO (mean (sd)) | 10.61 (4.39) | 8.35 (4.16) | 0.092 | |
| R_PR_dirtNUM_NO (mean (sd)) | 2.76 (2.30) | 3.11 (2.11) | 0.627 | |
| R_FAR_secNUM_NO (mean (sd)) | 3.22 (2.17) | 4.15 (2.80) | 0.226 | |
| R_FAR_pensinsecNUM_NO (mean (sd)) | 8.43 (4.90) | 12.10 (5.16) | 0.022 | |
| R_FARpenNUM_NO (mean (sd)) | 12.26 (5.99) | 8.65 (4.57) | 0.034 | |
| R_FAR_C_NUM_NO (mean (sd)) | 5.09 (2.27) | 5.55 (2.80) | 0.553 | |
| R_FAR_floorsolidNUM_NO (mean (sd)) | 6.04 (3.65) | 6.75 (3.21) | 0.507 | |
| R_BR_area2_NUM (mean (sd)) | 18.26 (9.99) | 16.10 (8.85) | 0.460 | |
| R_PR_areaNUM (mean (sd)) | 10.48 (7.88) | 12.40 (6.16) | 0.383 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 4.74 (2.12) | 13.35 (3.27) | <0.001 | |
| OUT_SOW_totremproNUM (mean (sd)) | 8.91 (5.80) | 17.85 (8.03) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 11.78 (6.69) | 15.35 (8.20) | 0.124 | |
| R_BR_sowspersection (%) | 0.509 | |||
| <20 | 2 ( 8.7) | 1 ( 5.0) | ||
| >100 | 3 (13.0) | 3 ( 15.0) | ||
| 20-50 | 5 (21.7) | 6 ( 30.0) | ||
| 50-100 | 9 (39.1) | 7 ( 35.0) | ||
| all | 4 (17.4) | 1 ( 5.0) | ||
| noinfo | 0 ( 0.0) | 2 ( 10.0) | ||
| R_BR_type (%) | 0.334 | |||
| 1 | 3 (13.0) | 1 ( 5.0) | ||
| 2 | 1 ( 4.3) | 1 ( 5.0) | ||
| 3 | 15 (65.2) | 16 ( 80.0) | ||
| 12 | 2 ( 8.7) | 0 ( 0.0) | ||
| 13 | 2 ( 8.7) | 0 ( 0.0) | ||
| 23 | 0 ( 0.0) | 1 ( 5.0) | ||
| 124 | 0 ( 0.0) | 1 ( 5.0) | ||
| R_BR_noise = 1 (%) | 13 (56.5) | 9 ( 45.0) | 0.654 | |
| R_BR_pest_NO = 1 (%) | 10 (43.5) | 11 ( 55.0) | 0.654 | |
| R_BR_airqual = 1 (%) | 7 (30.4) | 11 ( 55.0) | 0.187 | |
| R_BR_outC (%) | 0.374 | |||
| 13 (56.5) | 10 ( 50.0) | |||
| 10 | 1 ( 4.3) | 0 ( 0.0) | ||
| 13 | 1 ( 4.3) | 0 ( 0.0) | ||
| 14 | 0 ( 0.0) | 2 ( 10.0) | ||
| 15 | 1 ( 4.3) | 0 ( 0.0) | ||
| 18 | 1 ( 4.3) | 0 ( 0.0) | ||
| 19 | 1 ( 4.3) | 1 ( 5.0) | ||
| 2 | 0 ( 0.0) | 1 ( 5.0) | ||
| 20 | 0 ( 0.0) | 1 ( 5.0) | ||
| 23 | 0 ( 0.0) | 1 ( 5.0) | ||
| 24 | 0 ( 0.0) | 1 ( 5.0) | ||
| 25 | 4 (17.4) | 1 ( 5.0) | ||
| 28,3 | 0 ( 0.0) | 1 ( 5.0) | ||
| 31 | 0 ( 0.0) | 1 ( 5.0) | ||
| 8 | 1 ( 4.3) | 0 ( 0.0) | ||
| R_BR_floorbetmetplastwoodother = betmet (%) | 4 (17.4) | 8 ( 40.0) | 0.191 | |
| R_BR_floorsolid_0981_2 = 2 (%) | 5 (21.7) | 2 ( 10.0) | 0.531 | |
| R_BR_kuivaliete (%) | 0.769 | |||
| 1 | 6 (26.1) | 4 ( 20.0) | ||
| 2 | 15 (65.2) | 15 ( 75.0) | ||
| 12 | 2 ( 8.7) | 1 ( 5.0) | ||
| R_BR_PREGsame = 1 (%) | 8 (34.8) | 4 ( 21.1) | 0.524 | |
| R_PR_areapersow (%) | 0.593 | |||
| 1 | 4 (17.4) | 7 ( 35.0) | ||
| 2 | 8 (34.8) | 5 ( 25.0) | ||
| 3 | 5 (21.7) | 3 ( 15.0) | ||
| 4 | 6 (26.1) | 5 ( 25.0) | ||
| R_PR_nonoise = 1 (%) | 12 (52.2) | 12 ( 60.0) | 0.836 | |
| R_PR_air = 1 (%) | 2 ( 8.7) | 9 ( 45.0) | 0.018 | |
| R_PR_floorsolid_0791_2 = 2 (%) | 12 (52.2) | 5 ( 25.0) | 0.132 | |
| R_PR_bedmatyn = 1 (%) | 18 (78.3) | 12 ( 60.0) | 0.333 | |
| R_PR_bedmatamount (%) | 0.387 | |||
| 0 | 5 (21.7) | 8 ( 40.0) | ||
| 1 | 9 (39.1) | 4 ( 20.0) | ||
| 2 | 5 (21.7) | 3 ( 15.0) | ||
| 3 | 4 (17.4) | 5 ( 25.0) | ||
| R_PR_floornoslip = 1 (%) | 3 (13.0) | 2 ( 10.0) | 1.000 | |
| R_PR_dirtmed = 2 (%) | 9 (42.9) | 9 ( 50.0) | 0.901 | |
| R_FAR_pensize_med4.9 (%) | 0.340 | |||
| 0 ( 0.0) | 1 ( 5.0) | |||
| 1,0 | 11 (47.8) | 12 ( 60.0) | ||
| 2,0 | 12 (52.2) | 7 ( 35.0) | ||
| R_FAR_noise = 1 (%) | 11 (47.8) | 8 ( 40.0) | 0.836 | |
| R_FAR_airqual = 1 (%) | 2 ( 8.7) | 2 ( 10.0) | 1.000 | |
| R_FAR_floorsolid_all0_100_100_2_muu1 (%) | 0.132 | |||
| 0 | 6 (26.1) | 5 ( 25.0) | ||
| 1 | 13 (56.5) | 15 ( 75.0) | ||
| 2 | 4 (17.4) | 0 ( 0.0) | ||
| R_FAR_kuivaliete = 2 (%) | 17 (73.9) | 19 ( 95.0) | 0.146 | |
| R_FAR_kunto = 1 (%) | 1 ( 4.3) | 3 ( 15.0) | 0.501 | |
| R_FAR_nonslippery = 1 (%) | 5 (21.7) | 2 ( 10.0) | 0.531 | |
| OUT_SOW_mort_dic = 1 (%) | 0 ( 0.0) | 20 (100.0) | <0.001 | |
| OUT_SOW_totrem_dic = 1 (%) | 7 (30.4) | 15 ( 75.0) | 0.009 | |
| OUT_SOW_cull_dic = 1 (%) | 11 (47.8) | 10 ( 50.0) | 1.000 |
Yhteenveto poistojen mediaanin mukaan
#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.
table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')table2%>%
kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px" )| 0 | 1 | p | test | |
|---|---|---|---|---|
| n | 22 | 21 | ||
| R_BR_C_NUM_NO (mean (sd)) | 15.50 (9.06) | 13.62 (7.33) | 0.460 | |
| R_BR_floorsolid_NUM_NO (mean (sd)) | 8.09 (2.62) | 7.67 (2.65) | 0.600 | |
| R_PR_sectionsNUM_NO (mean (sd)) | 2.00 (1.51) | 1.95 (1.43) | 0.913 | |
| R_PR_sowsinsecNUM_NO (mean (sd)) | 18.59 (10.13) | 18.05 (9.90) | 0.860 | |
| R_PR_sowsNUM_NO (mean (sd)) | 11.32 (5.75) | 9.33 (5.99) | 0.274 | |
| R_PR_areapersow_NUM_NO (mean (sd)) | 11.59 (6.02) | 11.14 (7.04) | 0.823 | |
| R_PR_crareaNUM_NO (mean (sd)) | 6.68 (4.52) | 6.48 (5.04) | 0.889 | |
| R_PR_CNUM_NO (mean (sd)) | 7.32 (3.98) | 6.14 (2.35) | 0.248 | |
| R_PR_floorsolidNUM_NO (mean (sd)) | 9.73 (4.48) | 9.38 (4.38) | 0.799 | |
| R_PR_dirtNUM_NO (mean (sd)) | 2.75 (2.31) | 3.11 (2.11) | 0.620 | |
| R_FAR_secNUM_NO (mean (sd)) | 3.00 (2.23) | 4.33 (2.63) | 0.080 | |
| R_FAR_pensinsecNUM_NO (mean (sd)) | 9.36 (4.30) | 10.95 (6.17) | 0.331 | |
| R_FARpenNUM_NO (mean (sd)) | 10.45 (5.03) | 10.71 (6.29) | 0.882 | |
| R_FAR_C_NUM_NO (mean (sd)) | 5.23 (2.86) | 5.38 (2.16) | 0.844 | |
| R_FAR_floorsolidNUM_NO (mean (sd)) | 6.82 (3.54) | 5.90 (3.33) | 0.389 | |
| R_BR_area2_NUM (mean (sd)) | 18.41 (9.50) | 16.05 (9.44) | 0.418 | |
| R_PR_areaNUM (mean (sd)) | 12.45 (6.72) | 10.24 (7.50) | 0.313 | |
| OUT_SOW_mort_proNUM (mean (sd)) | 8.73 (5.55) | 8.76 (4.73) | 0.983 | |
| OUT_SOW_totremproNUM (mean (sd)) | 7.77 (5.46) | 18.62 (6.82) | <0.001 | |
| OUT_SOW_cullproNUM (mean (sd)) | 7.18 (3.57) | 20.00 (4.27) | <0.001 | |
| R_BR_sowspersection (%) | 0.821 | |||
| <20 | 1 ( 4.5) | 2 ( 9.5) | ||
| >100 | 3 (13.6) | 3 ( 14.3) | ||
| 20-50 | 5 (22.7) | 6 ( 28.6) | ||
| 50-100 | 8 (36.4) | 8 ( 38.1) | ||
| all | 4 (18.2) | 1 ( 4.8) | ||
| noinfo | 1 ( 4.5) | 1 ( 4.8) | ||
| R_BR_type (%) | 0.542 | |||
| 1 | 1 ( 4.5) | 3 ( 14.3) | ||
| 2 | 2 ( 9.1) | 0 ( 0.0) | ||
| 3 | 16 (72.7) | 15 ( 71.4) | ||
| 12 | 1 ( 4.5) | 1 ( 4.8) | ||
| 13 | 1 ( 4.5) | 1 ( 4.8) | ||
| 23 | 0 ( 0.0) | 1 ( 4.8) | ||
| 124 | 1 ( 4.5) | 0 ( 0.0) | ||
| R_BR_noise = 1 (%) | 10 (45.5) | 12 ( 57.1) | 0.645 | |
| R_BR_pest_NO = 1 (%) | 11 (50.0) | 10 ( 47.6) | 1.000 | |
| R_BR_airqual = 1 (%) | 12 (54.5) | 6 ( 28.6) | 0.157 | |
| R_BR_outC (%) | 0.366 | |||
| 10 (45.5) | 13 ( 61.9) | |||
| 10 | 0 ( 0.0) | 1 ( 4.8) | ||
| 13 | 1 ( 4.5) | 0 ( 0.0) | ||
| 14 | 1 ( 4.5) | 1 ( 4.8) | ||
| 15 | 0 ( 0.0) | 1 ( 4.8) | ||
| 18 | 1 ( 4.5) | 0 ( 0.0) | ||
| 19 | 0 ( 0.0) | 2 ( 9.5) | ||
| 2 | 1 ( 4.5) | 0 ( 0.0) | ||
| 20 | 1 ( 4.5) | 0 ( 0.0) | ||
| 23 | 1 ( 4.5) | 0 ( 0.0) | ||
| 24 | 0 ( 0.0) | 1 ( 4.8) | ||
| 25 | 4 (18.2) | 1 ( 4.8) | ||
| 28,3 | 0 ( 0.0) | 1 ( 4.8) | ||
| 31 | 1 ( 4.5) | 0 ( 0.0) | ||
| 8 | 1 ( 4.5) | 0 ( 0.0) | ||
| R_BR_floorbetmetplastwoodother = betmet (%) | 6 (27.3) | 6 ( 28.6) | 1.000 | |
| R_BR_floorsolid_0981_2 = 2 (%) | 3 (13.6) | 4 ( 19.0) | 0.946 | |
| R_BR_kuivaliete (%) | 0.185 | |||
| 1 | 4 (18.2) | 6 ( 28.6) | ||
| 2 | 15 (68.2) | 15 ( 71.4) | ||
| 12 | 3 (13.6) | 0 ( 0.0) | ||
| R_BR_PREGsame = 1 (%) | 7 (33.3) | 5 ( 23.8) | 0.733 | |
| R_PR_areapersow (%) | 0.108 | |||
| 1 | 6 (27.3) | 5 ( 23.8) | ||
| 2 | 5 (22.7) | 8 ( 38.1) | ||
| 3 | 7 (31.8) | 1 ( 4.8) | ||
| 4 | 4 (18.2) | 7 ( 33.3) | ||
| R_PR_nonoise = 1 (%) | 11 (50.0) | 13 ( 61.9) | 0.632 | |
| R_PR_air = 1 (%) | 5 (22.7) | 6 ( 28.6) | 0.929 | |
| R_PR_floorsolid_0791_2 = 2 (%) | 10 (45.5) | 7 ( 33.3) | 0.617 | |
| R_PR_bedmatyn = 1 (%) | 16 (72.7) | 14 ( 66.7) | 0.920 | |
| R_PR_bedmatamount (%) | 0.863 | |||
| 0 | 6 (27.3) | 7 ( 33.3) | ||
| 1 | 6 (27.3) | 7 ( 33.3) | ||
| 2 | 5 (22.7) | 3 ( 14.3) | ||
| 3 | 5 (22.7) | 4 ( 19.0) | ||
| R_PR_floornoslip = 1 (%) | 1 ( 4.5) | 4 ( 19.0) | 0.314 | |
| R_PR_dirtmed = 2 (%) | 8 (40.0) | 10 ( 52.6) | 0.639 | |
| R_FAR_pensize_med4.9 (%) | 0.491 | |||
| 0 ( 0.0) | 1 ( 4.8) | |||
| 1,0 | 13 (59.1) | 10 ( 47.6) | ||
| 2,0 | 9 (40.9) | 10 ( 47.6) | ||
| R_FAR_noise = 1 (%) | 12 (54.5) | 7 ( 33.3) | 0.274 | |
| R_FAR_airqual = 1 (%) | 2 ( 9.1) | 2 ( 9.5) | 1.000 | |
| R_FAR_floorsolid_all0_100_100_2_muu1 (%) | 0.379 | |||
| 0 | 4 (18.2) | 7 ( 33.3) | ||
| 1 | 15 (68.2) | 13 ( 61.9) | ||
| 2 | 3 (13.6) | 1 ( 4.8) | ||
| R_FAR_kuivaliete = 2 (%) | 19 (86.4) | 17 ( 81.0) | 0.946 | |
| R_FAR_kunto = 1 (%) | 1 ( 4.5) | 3 ( 14.3) | 0.566 | |
| R_FAR_nonslippery = 1 (%) | 4 (18.2) | 3 ( 14.3) | 1.000 | |
| OUT_SOW_mort_dic = 1 (%) | 10 (45.5) | 10 ( 47.6) | 1.000 | |
| OUT_SOW_totrem_dic = 1 (%) | 5 (22.7) | 17 ( 81.0) | <0.001 | |
| OUT_SOW_cull_dic = 1 (%) | 0 ( 0.0) | 21 (100.0) | <0.001 |
Yhteenveto joku hylkays mukaan
res_mca = MCA(medmca, quanti.sup = c(27,28),quali.sup=c(25:26), graph = FALSE)summary(res_mca)##
## Call:
## MCA(X = medmca, quanti.sup = c(27, 28), quali.sup = c(25:26),
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6
## Variance 0.277 0.148 0.121 0.103 0.094 0.084
## % of var. 15.813 8.477 6.920 5.881 5.373 4.809
## Cumulative % of var. 15.813 24.289 31.210 37.091 42.464 47.273
## Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Dim.12
## Variance 0.079 0.077 0.071 0.066 0.057 0.054
## % of var. 4.537 4.415 4.030 3.798 3.256 3.107
## Cumulative % of var. 51.810 56.225 60.255 64.053 67.310 70.416
## Dim.13 Dim.14 Dim.15 Dim.16 Dim.17 Dim.18
## Variance 0.054 0.053 0.044 0.043 0.041 0.036
## % of var. 3.088 3.001 2.536 2.438 2.329 2.052
## Cumulative % of var. 73.504 76.505 79.041 81.479 83.808 85.860
## Dim.19 Dim.20 Dim.21 Dim.22 Dim.23 Dim.24
## Variance 0.031 0.029 0.026 0.024 0.023 0.018
## % of var. 1.760 1.654 1.461 1.382 1.290 1.056
## Cumulative % of var. 87.620 89.275 90.735 92.118 93.407 94.463
## Dim.25 Dim.26 Dim.27 Dim.28 Dim.29 Dim.30
## Variance 0.018 0.014 0.012 0.010 0.010 0.007
## % of var. 1.034 0.819 0.679 0.596 0.550 0.393
## Cumulative % of var. 95.497 96.316 96.995 97.592 98.142 98.534
## Dim.31 Dim.32 Dim.33 Dim.34 Dim.35 Dim.36
## Variance 0.007 0.006 0.004 0.003 0.002 0.002
## % of var. 0.380 0.339 0.244 0.182 0.121 0.087
## Cumulative % of var. 98.914 99.254 99.498 99.679 99.800 99.888
## Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 0.001 0.000 0.000 0.000 0.000 0.000
## % of var. 0.065 0.023 0.016 0.008 0.000 0.000
## Cumulative % of var. 99.953 99.975 99.992 100.000 100.000 100.000
##
## Individuals (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | -0.514 2.220 0.143 | 0.125 0.246 0.008 |
## 2 | 0.698 4.098 0.223 | -0.245 0.942 0.027 |
## 3 | 0.950 7.584 0.540 | 0.244 0.937 0.036 |
## 4 | -0.632 3.352 0.369 | 0.149 0.349 0.021 |
## 5 | -0.536 2.418 0.073 | 1.436 32.332 0.524 |
## 6 | 1.155 11.204 0.587 | 0.196 0.600 0.017 |
## 7 | -0.569 2.721 0.179 | 0.159 0.397 0.014 |
## 8 | 0.153 0.197 0.027 | 0.187 0.551 0.041 |
## 9 | -0.028 0.007 0.001 | -0.457 3.276 0.208 |
## 10 | 0.008 0.000 0.000 | -0.498 3.893 0.215 |
## Dim.3 ctr cos2
## 1 0.292 1.639 0.046 |
## 2 0.624 7.475 0.178 |
## 3 0.036 0.025 0.001 |
## 4 0.141 0.381 0.018 |
## 5 -0.231 1.029 0.014 |
## 6 0.365 2.564 0.059 |
## 7 0.598 6.872 0.198 |
## 8 -0.119 0.273 0.016 |
## 9 -0.353 2.391 0.124 |
## 10 0.025 0.012 0.001 |
##
## Categories (the 10 first)
## Dim.1 ctr cos2 v.test Dim.2 ctr cos2
## <20 | 1.753 3.228 0.230 3.111 | 0.664 0.865 0.033
## >100 | -0.396 0.329 0.025 -1.032 | -0.723 2.046 0.085
## 20-50 | 0.030 0.004 0.000 0.116 | -0.313 0.703 0.034
## 50-100 | -0.490 1.346 0.142 -2.446 | -0.088 0.080 0.005
## all | 1.318 3.042 0.229 3.099 | 0.206 0.138 0.006
## noinfo | -0.984 0.678 0.047 -1.408 | 3.079 12.383 0.462
## R_BR_type_1 | 1.606 3.613 0.265 3.334 | 0.126 0.042 0.002
## R_BR_type_2 | 1.902 2.534 0.176 2.723 | 0.367 0.176 0.007
## R_BR_type_3 | -0.422 1.936 0.461 -4.399 | -0.046 0.043 0.005
## R_BR_type_12 | 0.829 0.482 0.034 1.187 | -0.363 0.172 0.006
## v.test Dim.3 ctr cos2 v.test
## <20 1.179 | -0.393 0.370 0.012 -0.697 |
## >100 -1.886 | -0.299 0.429 0.014 -0.780 |
## 20-50 -1.189 | 0.377 1.251 0.049 1.433 |
## 50-100 -0.437 | -0.028 0.010 0.000 -0.141 |
## all 0.484 | 0.293 0.343 0.011 0.688 |
## noinfo 4.407 | -1.094 1.914 0.058 -1.565 |
## R_BR_type_1 0.262 | 0.946 2.862 0.092 1.963 |
## R_BR_type_2 0.525 | -1.015 1.650 0.050 -1.453 |
## R_BR_type_3 -0.479 | 0.002 0.000 0.000 0.023 |
## R_BR_type_12 -0.520 | 1.054 1.777 0.054 1.509 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## R_BR_sowspersection | 0.573 0.577 0.126 |
## R_BR_type | 0.610 0.057 0.391 |
## R_BR_noise | 0.085 0.229 0.071 |
## R_BR_airqual | 0.115 0.213 0.004 |
## R_BR_floorbetmetplastwoodother | 0.170 0.000 0.046 |
## R_BR_floorsolid_0981_2 | 0.620 0.040 0.013 |
## R_BR_kuivaliete | 0.559 0.028 0.136 |
## R_BR_PREGsame | 0.305 0.142 0.188 |
## R_PR_areapersow | 0.407 0.238 0.181 |
## R_PR_nonoise | 0.028 0.272 0.074 |
##
## Supplementary categories
## Dim.1 cos2 v.test Dim.2 cos2 v.test
## OUT_SOW_totrem_dic_0 | 0.343 0.113 2.174 | -0.125 0.015 -0.792 |
## OUT_SOW_totrem_dic_1 | -0.328 0.113 -2.174 | 0.119 0.015 0.792 |
## OUT_SOW_cull_dic_0 | 0.050 0.003 0.334 | -0.031 0.001 -0.202 |
## OUT_SOW_cull_dic_1 | -0.053 0.003 -0.334 | 0.032 0.001 0.202 |
## Dim.3 cos2 v.test
## OUT_SOW_totrem_dic_0 -0.030 0.001 -0.192 |
## OUT_SOW_totrem_dic_1 0.029 0.001 0.192 |
## OUT_SOW_cull_dic_0 -0.138 0.020 -0.913 |
## OUT_SOW_cull_dic_1 0.144 0.020 0.913 |
##
## Supplementary categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## OUT_SOW_totrem_dic | 0.113 0.015 0.001 |
## OUT_SOW_cull_dic | 0.003 0.001 0.020 |
##
## Supplementary continuous variables
## Dim.1 Dim.2 Dim.3
## OUT_SOW_mort_proNUM | -0.379 | 0.166 | -0.085 |
## OUT_SOW_cullproNUM | -0.083 | 0.095 | 0.101 |To visualize the percentage of inertia explained by each MCA dimension:
eig.val <- res_mca$eig
barplot(eig.val[, 2],
names.arg = 1:nrow(eig.val),
main = "Variances Explained by Dimensions (%)",
xlab = "Principal Dimensions",
ylab = "Percentage of variances",
col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2],
type = "b", pch = 19, col = "red")
res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
var_sup = TRUE, var_lab_min_contrib = 0,
col_var = "Variable", symbol_var = "Type",
size_var = NULL, size_range = c(10, 300),
labels_size = 10, point_size = 56,
transitions = TRUE, labels_positions = NULL)res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
lab_var = NULL, , ind_lab_min_contrib = 0,
col_var = NULL, labels_size = 9,
point_opacity = 0.5, opacity_var = NULL, point_size = 64,
ellipses = FALSE, transitions = TRUE, labels_positions = NULL)fviz_mca_var(res_mca, choice = "quanti.sup",
ggtheme = theme_minimal())
## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```
##
## ```
## drawn <-
## c("6", "5", "3", "39", "18", "4", "27", "17", "13", "21")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
##
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
##
##
## The cluster 1 is made of individuals such as*. This group is characterized by4* and *4*. :
##
## - high frequency for factors like *R_PR_bedmatamount=R_PR_bedmatamount_0*, *R_PR_bedmatyn=R_PR_bedmatyn_0*, *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_0*, *R_PR_air=R_PR_air_1*, *R_BR_type=R_BR_type_3*, *R_FAR_pensize_med4.9=1,0*, *R_FAR_airqual=R_FAR_airqual_1*, *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_1*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_1* and *R_BR_PREGsame=R_BR_PREGsame_0* (factors are sorted from the most common).
## - low frequency for factors like *R_PR_bedmatyn=R_PR_bedmatyn_1*, *R_FAR_pensize_med4.9=2,0*, *R_PR_air=R_PR_air_0*, *R_BR_PREGsame=R_BR_PREGsame_1*, *R_FAR_airqual=R_FAR_airqual_0*, *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_0*, *R_PR_bedmatamount=R_PR_bedmatamount_3*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_2*, *R_PR_bedmatamount=R_PR_bedmatamount_1* and *R_PR_bedmatamount=R_PR_bedmatamount_2* (factors are sorted from the rarest).
##
## The 1st cluster is made of individuals such as *17*. This group is characterized by :
##
## - high frequency for the factors *R_PR_bedmatyn=R_PR_bedmatyn_1*, *R_PR_air=R_PR_air_0*, *R_PR_bedmatamount=R_PR_bedmatamount_3*, *R_FAR_kuivaliete=R_FAR_kuivaliete_2*, *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_1* and *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_1* (factors are sorted from the most common).
## - low frequency for the factors *R_PR_bedmatyn=R_PR_bedmatyn_0*, *R_PR_bedmatamount=R_PR_bedmatamount_0*, *R_PR_air=R_PR_air_1*, *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_2*, *R_FAR_kuivaliete=R_FAR_kuivaliete_1* and *R_BR_kuivaliete=R_BR_kuivaliete_1* (factors are sorted from the rarest).
##
## The cluster 3 is made of individuals such as*. This group is characterized by3* and *3*. :
##
## - high frequency for factors like *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_2*, *R_BR_kuivaliete=R_BR_kuivaliete_1*, *R_FAR_kuivaliete=R_FAR_kuivaliete_1*, *R_PR_dirtmed=R_PR_dirtmed_1*, *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_2*, *R_BR_type=R_BR_type_1*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_2*, *R_PR_areapersow=R_PR_areapersow_4*, *R_BR_sowspersection=all* and *R_BR_sowspersection=<20* (factors are sorted from the most common).
## - low frequency for factors like *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_1*, *R_BR_kuivaliete=R_BR_kuivaliete_2*, *R_BR_type=R_BR_type_3*, *R_FAR_kuivaliete=R_FAR_kuivaliete_2*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_1*, *R_PR_dirtmed=R_PR_dirtmed_2*, *R_BR_sowspersection=50-100*, *R_BR_PREGsame=R_BR_PREGsame_0*, *R_PR_bedmatyn=R_PR_bedmatyn_0* and *R_PR_bedmatamount=R_PR_bedmatamount_0* (factors are sorted from the rarest).## **Results for the Hierarchical Clustering on Principal Components**
## name
## 1 "$data.clust"
## 2 "$desc.var"
## 3 "$desc.var$test.chi2"
## 4 "$desc.axes$category"
## 5 "$desc.axes"
## 6 "$desc.axes$quanti.var"
## 7 "$desc.axes$quanti"
## 8 "$desc.ind"
## 9 "$desc.ind$para"
## 10 "$desc.ind$dist"
## 11 "$call"
## 12 "$call$t"
## description
## 1 "dataset with the cluster of the individuals"
## 2 "description of the clusters by the variables"
## 3 "description of the cluster var. by the categorical var."
## 4 "description of the clusters by the categories."
## 5 "description of the clusters by the dimensions"
## 6 "description of the cluster var. by the axes"
## 7 "description of the clusters by the axes"
## 8 "description of the clusters by the individuals"
## 9 "parangons of each clusters"
## 10 "specific individuals"
## 11 "summary statistics"
## 12 "description of the tree"